The present invention relates generally to the analysis of unstructured data, and more specifically to a system for identifying common patterns within unstructured data elements.
With the abundance of unstructured data made available through various means in general and the Internet in particular, there is also a need to provide effective ways of analyzing such data. Unstructured data analysis is a challenging task, as it requires processing of big-data. Big data typically refers to a collection of data sets that are large and complex and cannot be analyzed using on-hand database management tools or traditional data processing applications.
Several prior art solutions can be used to search through big data sources. As a result of the search, relevant data elements may be extracted from such big data sources. However, a problem may occur while trying to search for additional data that may be useful, for example, data containing similar characteristics to the characteristics of the extracted data. Typically, the complexity of the data while analyzing the characteristics of big data, leads to inefficient identification of common patterns. Furthermore, the search as known today may be inefficient because of lack of correlation between data elements extracted from big data sources.
It would be therefore advantageous to provide an efficient solution to analyze big data. It would be further advantageous if such solution would enable correlating between common patterns while analyzing the big data.
Certain embodiments disclosed herein include a method for detection of common patterns within unstructured data elements. The method includes searching a plurality of unstructured data elements extracted from big data sources to identify a plurality of patches; extracting the identified plurality of patches; generating, by a signature generator system, at least one signature for each patch, wherein the signature generator system includes a plurality of computational cores configured to receive the plurality of patches, each computational core of the plurality of computational cores having properties that are at least partly statistically independent of other of the computational cores, wherein the properties are set independently of each other core; identifying common patterns among the at least one generated signature; clustering the signatures having the identified common patterns; and correlating the generated clusters to identify associations between the respective identified common patterns.
Certain embodiments disclosed herein also include a system for analyzing unstructured data. The system includes a network interface for allowing connectivity to a plurality of big data sources; a processing unit; and a memory connected to the processing unit, the memory containing instructions that, when executed by the processing unit, configure the system to: search a plurality of unstructured data elements extracted from the big data sources to identify a plurality of patches; extract the identified plurality of patches; generate, by a signature generator system, at least one signature for each patch, wherein the signature generator system includes a plurality of computational cores configured to receive the plurality of patches, each computational core of the plurality of computational cores having properties that are at least partly statistically independent of other of the computational cores, wherein the properties are set independently of each other core; identify common patterns among the at least one generated signature; cluster the signatures having the identified common patterns; and correlate the generated clusters to identify associations between the respective identified common patterns.
The subject matter that is regarded as the invention is particularly pointed out and distinctly claimed in the claims at the conclusion of the specification. The foregoing and other objects, features, and advantages of the invention will be apparent from the following detailed description taken in conjunction with the accompanying drawings.
It is important to note that the embodiments disclosed herein are only examples of the many advantageous uses of the innovative teachings herein. In general, statements made in the specification of the present application do not necessarily limit any of the various claimed inventions. Moreover, some statements may apply to some inventive features but not to others. In general, unless otherwise indicated, singular elements may be in plural and vice versa with no loss of generality. In the drawings, like numerals refer to like parts through several views.
Certain exemplary embodiments disclosed herein allow determining a correlation between unstructured data elements extracted from big data sources comprising unstructured data. The correlation refers to any of a broad class of statistical relationships involving at least two sets of data. The embodiments disclosed herein determine the nature of the relationship between the unstructured data elements. One or more common patterns are identified within the unstructured data elements respective of a signature analysis. Unstructured data refers to pieces of data that do not have a predefined structure and are usually not organized in a consistent and predictable manner.
Typically, the data tends to be recorded in a free text form with little or no metadata codified into fields. Unstructured data may be, for example, a multimedia content, a book, a document, metadata, health records, audio, video, analog data, files, unstructured text, web page, a combination thereof, a portion thereof, and so on. The method described herein determines an association of the unstructured data elements in space and/or time respective of the big data sources.
A big data analyzer, for example, the server 130 is connected to the network 110. The server 130 is configured to correlate between unstructured data elements extracted from big data sources comprising unstructured data as described in detail below. The server 130 typically comprises a processing unit, such as a processor (not shown) that is communicatively connected to a memory. The memory contains instructions that are executed by the processor. The server 130 also includes an interface (not shown) to the network 110.
In one embodiment, a database such as, a data warehouse 150 is connected to the server 130 (either directly or through the network 110). The server 130 is configured to store in the data warehouse 150 information identified and/or generated by the server 130 for further use. Such information may include, signatures generated for the unstructured data elements, common patterns identified between the unstructured data elements, common concepts identified between the common patterns, and so on, as described in greater detail with respect of
Further connected to the network 110 are a plurality of big data sources 120-1 through 120-n, each of which may contain, store, or generate unstructured data. The big data sources 120-1 through 120-n are accessible by the server 130 through the network 110. The system 100 also includes a signature generator system (SGS) 140. In one embodiment, the SGS 140 is connected to the server 130. The server 130 is configured to receive and serve the unstructured data elements. Moreover, the server 130 is configured to cause the SGS 140 to generate the signatures respective of the unstructured data elements. Each signature is generated for each element of the unstructured data.
The SGS 140 typically comprises a processing unit and a memory maintaining executable instructions. Such instructions may be executed by the processor. The process for generating the signatures for the unstructured data elements, is explained in more detail herein below with respect to
According to the various exemplary embodiments, the unstructured data may include multimedia content elements. A multimedia content element may be, for example, an image, a graphic, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, and an image of signals (e.g., spectrograms, phasograms, scalograms, etc.), and/or combinations thereof and portions thereof.
The server 130 is configured to extract unstructured data elements from at least one big data source 120 and provide such elements to the SGS 140. The SGS 140 is further configured to generate at least one signature for each extracted unstructured data element. The generated signature(s) may be robust to noise and distortion as discussed below with respect to
In one embodiment, the signatures generated for more than one unstructured data element are clustered. The clustered signatures are used to search for a common concept. The concept is a collection of signatures representing elements of the unstructured data and metadata describing the concept. As a non-limiting example, a ‘Superman concept’ is a signature reduced cluster of signatures describing elements (such as multimedia elements) related to, e.g., a Superman cartoon: a set of metadata representing proving textual representation of the Superman concept. Techniques for generating concepts and concept structures are also described in the above-referenced U.S. Pat. No. 8,266,185 to Raichelgauz et al., which is assigned to common assignee, and is incorporated hereby by reference for all that it contains.
In S210, unstructured data is received or retrieved from a plurality of big data sources, e.g. sources 120-1, 120-n. The sources may be classified by the data contained therein. For example, big data sources 120-1, 120-n may be related to all images found on the Internet, diagnostic information of a large group of patients, sales information of a large group of retail stores, and so on. As noted above, the unstructured data may be, for example, a multimedia content, a book, a document, metadata, health records, audio, video, analog data, files, unstructured text, web pages, a combination thereof, a portion thereof, and so on. In one embodiment, the processing is separately performed on the unstructured data retrieved/received from big data sources having the same classification. For example, all the images in the Internet would be processed separately from e.g. medical records.
In S220, unstructured data elements are extracted from the data collected from the big data sources, e.g. sources 120-1, 120-n. In one embodiment, the extracted elements are of specific interest, or otherwise of higher interest than other elements comprised in the collected unstructured data. As an example, a product's attributes and the sales volume of the product may be of interest. As another example, a disease type and its characteristics may be of higher interest than the patients' names. In addition, certain keywords may be of specific interest. As yet another example, portions of a multimedia element (e.g., a picture) having entropy level over a predefined threshold may be of more interest than other portions. In one non-limiting embodiment, S220 includes searching patterns or patches in the unstructured data, and extracting such identified patterns or patches. Typically, a patch of an image is defined by, for example, its size, scale, location and orientation. A video/audio patch may be a 1%) of the total length of video/audio clip.
In S230, at least one signature for each extracted unstructured data element is generated. In one embodiment, each of the at least one signature is robust to noise and/or distortion and is generated by the SGS 140 as described below. In S240, it is checked if the number of extracted unstructured data elements are above a predefined threshold, i.e., if there is sufficient information for the processing, and if so, execution continues with S250; otherwise, execution continues with S220.
In S250, the generated signatures are analyzed to identify common patterns among the generated signatures. In one embodiment, a process of inter-matching is performed on the generated signatures. In an exemplary embodiment, this process includes matching signatures of all the extracted elements to each other. Each match of two signatures is assigned with a matching score being compared to a preconfigured threshold. When the matching score exceeds the preconfigured threshold, the two signatures are determined to have common pattern.
In S260, the signatures determined to have common patterns are clustered. In an embodiment, the clustering of the signatures is performed as discussed in detail in the U.S. Pat. No. 8,386,400, entitled “Unsupervised Clustering of Multimedia Data Using a Large-Scale Matching System,” filed on Jul. 22, 2009, assigned to common assignee, and which is hereby incorporated for all that it contains. It should be noted that S250 and S260 can result in a plurality of different clusters. As noted above, a cluster may include a textual metadata.
In S270, a correlation between the created clusters is performed to detect association between common patterns identified in the respective clusters. For example, if the common pattern of cluster A is ‘red roses’ and the common pattern of cluster B is ‘Valentine's Day’, then the correlation would detect any association and/or relationship between ‘red roses’ and ‘Valentine's Day’. In one embodiment, a preconfigured threshold level is used to determine if there is an association between at least two clusters of the created clusters. This preconfigured threshold defines at least a number of signatures to be found in two or more correlated-clusters in order to determine that there is an association between the clusters. The correlation can be performed between each two different clusters of the created clusters.
In S280, a concept structure (or concept) is generated by re-clustering two or more clusters determined to have some sort of association between their patterns. In one embodiment, S280 may also include a process for reduction of the number of signatures in the re-clustered cluster and adding of the respective metadata to the reduced clusters to form a concept structure. In S280, the generated signatures, the concept, the clusters, and the identified common patterns may be stored in a data warehouse 150 for further use. In S290, it is checked whether additional unstructured data is received or retrieved, and if so, execution continues with S210; otherwise, execution terminates.
As a non-limiting example, several sales reports of worldwide retail chain stores are received by the server 130. The reports are analyzed and signatures are generated respective of each element within the reports. An element within the reports may be, for example, a certain product, or a certain product together with the quantity sold. Respective of the generated signatures, common patterns are identified, and then clustered as described above. For example, a first common pattern of a first cluster of signatures indicates that every certain date a significant amount of products which are packed in red packages are being sold. A second common pattern of second cluster of signatures indicates that an extensive amount of jewelry is sold in February. A third common pattern of a third cluster of signatures indicates an increase in sales of alcoholic beverages on the eve of February 14. Upon correlating between the plurality of clusters, consumption habits can be determined with respect to Valentine's Day. By analyzing the common patterns of the sales report, the server 130 enables determination of a common concept related to the plurality of common patterns. In this case, the common concept may be “during Valentine's Day people tend to spend more money.”
Video content segments 2 from a Master database (DB) 6 and a Target DB 1 are processed in parallel by a large number of independent computational Cores 3 that constitute an architecture for generating the Signatures (hereinafter the “Architecture”). Further details on the computational Cores generation are provided below. The independent Cores 3 generate a database of Robust Signatures and Signatures 4 for Target content-segments 5 and a database of Robust Signatures and Signatures 7 for Master content-segments 8. An exemplary and non-limiting process of signature generation for an audio component is shown in detail in
To demonstrate an example of signature generation process, it is assumed, merely for the sake of simplicity and without limitation on the generality of the disclosed embodiments, that the signatures are based on a single frame, leading to certain simplification of the computational cores generation. The Matching System is extensible for signatures generation capturing the dynamics in-between the frames.
The Signatures' generation process will now be described with reference to
In order to generate Robust Signatures, i.e., Signatures that are robust to additive noise L (where L is an integer equal to or greater than 1) by the Computational Cores 3 a frame T is injected into all the Cores 3. Then, Cores 3 generate two binary response vectors: S which is a Signature vector, and RS which is a Robust Signature vector.
For generation of signatures robust to additive noise, such as White-Gaussian-Noise, scratch, etc., but not robust to distortions, such as crop, shift and rotation, etc., a core Ci={ni} (1≤i≤L) may consist of a single leaky integrate-to-threshold unit (LTU) node or more nodes. The node ni equations are:
n
i
=(Vi−Thx)
where, is a Heaviside step function; wij is a coupling node unit (CNU) between node i and image component j (for example, grayscale value of a certain pixel j); kj is an image component (for example, grayscale value of a certain pixel j); Thx is a constant Threshold value, where x is ‘S’ for Signature and ‘RS’ for Robust Signature; and Vi is a Coupling Node Value.
The Threshold values Thx are set differently for Signature generation and for Robust Signature generation. For example, for a certain distribution of Vi values (for the set of nodes), the thresholds for Signature (ThS) and Robust Signature (ThRS) are set apart, after optimization, according to at least one or more of the following criteria:
i.e., given that /nodes (cores) constitute a Robust Signature of a certain image I, the probability that not all of these I nodes will belong to the Signature of same, but noisy image, Ĩ is sufficiently low (according to a system's specified accuracy).
It should be understood that the generation of a signature is unidirectional, and typically yields lossless compression, where the characteristics of the compressed data are maintained but the uncompressed data cannot be reconstructed. Therefore, a signature can be used for the purpose of comparison to another signature without the need of comparison to the original data. Detailed description of the Signature generation can be found U.S. Pat. Nos. 8,326,775 and 8,312,031, assigned to common assignee, which are hereby incorporated by reference for all the useful information they contain.
A Computational Core generation is a process of definition, selection, and tuning of the parameters of the cores for a certain realization in a specific system and application. The process is based on several design considerations, such as:
(a) The Cores should be designed so as to obtain maximal independence, i.e., the projection from a signal space should generate a maximal pair-wise distance between any two cores' projections into a high-dimensional space.
(b) The Cores should be optimally designed for the type of signals, i.e., the Cores should be maximally sensitive to the spatio-temporal structure of the injected signal, for example, and in particular, sensitive to local correlations in time and space. Thus, in some cases a core represents a dynamic system, such as in state space, phase space, edge of chaos, etc., which is uniquely used herein to exploit their maximal computational power.
(c) The Cores should be optimally designed with regard to invariance to a set of signal distortions, of interest in relevant applications. Detailed description of the Computational Core generation, the computational architecture, and the process for configuring such cores is discussed in more detail in the above-referenced U.S. Pat. No. 8,655,801.
The various embodiments disclosed herein can be implemented as hardware, firmware, software, or any combination thereof. Moreover, the software is preferably implemented as an application program tangibly embodied on a program storage unit or non-transitory computer readable medium consisting of parts, or of certain devices and/or a combination of devices. The application program may be uploaded to, and executed by, a machine comprising any suitable architecture. Preferably, the machine is implemented on a computer platform having hardware such as one or more central processing units (“CPUs”), a memory, and input/output interfaces. The computer platform may also include an operating system and microinstruction code. The various processes and functions described herein may be either part of the microinstruction code or part of the application program, or any combination thereof, which may be executed by a CPU, whether or not such a computer or processor is explicitly shown. In addition, various other peripheral units may be connected to the computer platform such as an additional data storage unit and a printing unit. Furthermore, a non-transitory computer readable medium is any computer readable medium except for a transitory propagating signal.
All examples and conditional language recited herein are intended for pedagogical purposes to aid the reader in understanding the principles of the invention and the concepts contributed by the inventor to furthering the art, and are to be construed as being without limitation to such specifically recited examples and conditions. Moreover, all statements herein reciting principles, aspects, and embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future, i.e., any elements developed that perform the same function, regardless of structure.
This application is a continuation of U.S. patent application Ser. No. 14/013,740 filed on Aug. 29, 2013, now allowed, which claims the benefit of U.S. provisional application No. 61/773,838 filed on Mar. 7, 2013. The Ser. No. 14/013,740 application is a continuation-in-part (CIP) of U.S. patent application Ser. No. 13/602,858 filed on Sep. 4, 2012, now U.S. Pat. No. 8,868,619, which is a continuation of U.S. patent application Ser. No. 12/603,123, filed on Oct. 21, 2009, now U.S. Pat. No. 8,266,185, which is a continuation-in-part of: (1) U.S. patent application Ser. No. 12/084,150 having a filing date of Apr. 7, 2009, now U.S. Pat. No. 8,655,801, which is the National Stage of International Application No. PCT/IL2006/001235, filed on Oct. 26, 2006, which claims foreign priority from Israeli Application No. 171577 filed on Oct. 26, 2005 and Israeli Application No. 173409 filed on Jan. 29, 2006; (2) U.S. patent application Ser. No. 12/195,863 filed on Aug. 21, 2008, now U.S. Pat. No. 8,326,775, which claims priority under 35 USC 119 from Israeli Application No. 185414, filed on Aug. 21, 2007, and which is also a continuation-in-part of the above-referenced U.S. patent application Ser. No. 12/084,150; (3) U.S. patent application Ser. No. 12/348,888 filed on Jan. 5, 2009, now pending, which is a CIP of the above-referenced U.S. patent application Ser. Nos. 12/084,150 and 12/195,863; and (4) U.S. patent application Ser. No. 12/538,495 filed on Aug. 10, 2009, now U.S. Pat. No. 8,312,031, which is a CIP of the above-referenced U.S. patent application Ser. Nos. 12/084,150; 12/195,863; and 12/348,888. All of the applications referenced above are herein incorporated by reference for all that they contain.
Number | Name | Date | Kind |
---|---|---|---|
4733353 | Jaswa | Mar 1988 | A |
4932645 | Schorey et al. | Jun 1990 | A |
4972363 | Nguyen et al. | Nov 1990 | A |
5307451 | Clark | Apr 1994 | A |
5568181 | Greenwood et al. | Oct 1996 | A |
5745678 | Herzberg et al. | Apr 1998 | A |
5806061 | Chaudhuri et al. | Sep 1998 | A |
5852435 | Vigneaux et al. | Dec 1998 | A |
5870754 | Dimitrova et al. | Feb 1999 | A |
5873080 | Coden et al. | Feb 1999 | A |
5887193 | Takahashi et al. | Mar 1999 | A |
5940821 | Wical | Aug 1999 | A |
5978754 | Kumano | Nov 1999 | A |
5987454 | Hobbs | Nov 1999 | A |
6038560 | Wical | Mar 2000 | A |
6052481 | Grajski et al. | Apr 2000 | A |
6076088 | Paik et al. | Jun 2000 | A |
6122628 | Castelli et al. | Sep 2000 | A |
6137911 | Zhilyaev | Oct 2000 | A |
6144767 | Bottou et al. | Nov 2000 | A |
6147636 | Gershenson | Nov 2000 | A |
6240423 | Hirata | May 2001 | B1 |
6243375 | Speicher | Jun 2001 | B1 |
6243713 | Nelson et al. | Jun 2001 | B1 |
6275599 | Adler et al. | Aug 2001 | B1 |
6329986 | Cheng | Dec 2001 | B1 |
6363373 | Steinkraus | Mar 2002 | B1 |
6381656 | Shankman | Apr 2002 | B1 |
6422617 | Fukumoto et al. | Jul 2002 | B1 |
6493692 | Kobayashi et al. | Dec 2002 | B1 |
6493705 | Kobayashi et al. | Dec 2002 | B1 |
6507672 | Watkins et al. | Jan 2003 | B1 |
6523022 | Hobbs | Feb 2003 | B1 |
6523046 | Liu et al. | Feb 2003 | B2 |
6524861 | Anderson | Feb 2003 | B1 |
6526400 | Takata et al. | Feb 2003 | B1 |
6550018 | Abonamah et al. | Apr 2003 | B1 |
6560597 | Dhillon et al. | May 2003 | B1 |
6594699 | Sahai et al. | Jul 2003 | B1 |
6601060 | Tomaru | Jul 2003 | B1 |
6611628 | Sekiguchi et al. | Aug 2003 | B1 |
6611837 | Schreiber | Aug 2003 | B2 |
6618711 | Ananth | Sep 2003 | B1 |
6643620 | Contolini et al. | Nov 2003 | B1 |
6643643 | Lee et al. | Nov 2003 | B1 |
6675159 | Lin et al. | Jan 2004 | B1 |
6704725 | Lee | Mar 2004 | B1 |
6728706 | Aggarwal et al. | Apr 2004 | B2 |
6732149 | Kephart | May 2004 | B1 |
6751363 | Natsev et al. | Jun 2004 | B1 |
6754435 | Kim | Jun 2004 | B2 |
6763069 | Divakaran et al. | Jul 2004 | B1 |
6763519 | McColl et al. | Jul 2004 | B1 |
6774917 | Foote et al. | Aug 2004 | B1 |
6795818 | Lee | Sep 2004 | B1 |
6804356 | Krishnamachari | Oct 2004 | B1 |
6819797 | Smith et al. | Nov 2004 | B1 |
6836776 | Schreiber | Dec 2004 | B2 |
6845374 | Oliver et al. | Jan 2005 | B1 |
6901207 | Watkins | May 2005 | B1 |
6938025 | Lulich et al. | Aug 2005 | B1 |
6970881 | Mohan et al. | Nov 2005 | B1 |
6978264 | Chandrasekar et al. | Dec 2005 | B2 |
7006689 | Kasutani | Feb 2006 | B2 |
7013051 | Sekiguchi et al. | Mar 2006 | B2 |
7043473 | Rassool et al. | May 2006 | B1 |
7124149 | Smith et al. | Oct 2006 | B2 |
7158681 | Persiantsev | Jan 2007 | B2 |
7199798 | Echigo et al. | Apr 2007 | B1 |
7215828 | Luo | May 2007 | B2 |
7260564 | Lynn et al. | Aug 2007 | B1 |
7277928 | Lennon | Oct 2007 | B2 |
7296012 | Ohashi | Nov 2007 | B2 |
7302117 | Sekiguchi et al. | Nov 2007 | B2 |
7313805 | Rosin et al. | Dec 2007 | B1 |
7346629 | Kapur et al. | Mar 2008 | B2 |
7392238 | Zhou et al. | Jun 2008 | B1 |
7406459 | Chen et al. | Jul 2008 | B2 |
7450740 | Shah et al. | Nov 2008 | B2 |
7523102 | Bjarnestam et al. | Apr 2009 | B2 |
7526607 | Singh et al. | Apr 2009 | B1 |
7536384 | Venkataraman et al. | May 2009 | B2 |
7542969 | Rappaport et al. | Jun 2009 | B1 |
7548910 | Chu et al. | Jun 2009 | B1 |
7555477 | Bayley et al. | Jun 2009 | B2 |
7555478 | Bayley et al. | Jun 2009 | B2 |
7562076 | Kapur | Jul 2009 | B2 |
7574436 | Kapur et al. | Aug 2009 | B2 |
7574668 | Nunez et al. | Aug 2009 | B2 |
7577656 | Kawai et al. | Aug 2009 | B2 |
7657100 | Gokturk et al. | Feb 2010 | B2 |
7660468 | Gokturk et al. | Feb 2010 | B2 |
7694318 | Eldering et al. | Apr 2010 | B2 |
7836054 | Kawai et al. | Nov 2010 | B2 |
7920894 | Wyler | Apr 2011 | B2 |
7921107 | Chang et al. | Apr 2011 | B2 |
7933407 | Keidar et al. | Apr 2011 | B2 |
7974994 | Li et al. | Jul 2011 | B2 |
7987194 | Walker et al. | Jul 2011 | B1 |
7987217 | Long et al. | Jul 2011 | B2 |
7991715 | Schiff et al. | Aug 2011 | B2 |
8000655 | Wang et al. | Aug 2011 | B2 |
8023739 | Hohimer et al. | Sep 2011 | B2 |
8036893 | Reich | Oct 2011 | B2 |
8098934 | Vincent et al. | Jan 2012 | B2 |
8112376 | Raichelgauz et al. | Feb 2012 | B2 |
8260764 | Gruber | Sep 2012 | B1 |
8266148 | Guha et al. | Sep 2012 | B2 |
8266185 | Raichelgauz et al. | Sep 2012 | B2 |
8312031 | Raichelgauz et al. | Nov 2012 | B2 |
8315442 | Gokturk et al. | Nov 2012 | B2 |
8316005 | Moore | Nov 2012 | B2 |
8326775 | Raichelgauz et al. | Dec 2012 | B2 |
8345982 | Gokturk et al. | Jan 2013 | B2 |
8396892 | Loftus et al. | Mar 2013 | B2 |
8396894 | Jacobson et al. | Mar 2013 | B2 |
8504553 | Vailaya et al. | Aug 2013 | B2 |
8548828 | Longmire | Oct 2013 | B1 |
8655801 | Raichelgauz et al. | Feb 2014 | B2 |
8677377 | Cheyer et al. | Mar 2014 | B2 |
8682667 | Haughay | Mar 2014 | B2 |
8688446 | Yanagihara | Apr 2014 | B2 |
8706503 | Cheyer et al. | Apr 2014 | B2 |
8775442 | Moore et al. | Jul 2014 | B2 |
8799195 | Raichelgauz et al. | Aug 2014 | B2 |
8799196 | Raichelquaz et al. | Aug 2014 | B2 |
8818916 | Raichelgauz et al. | Aug 2014 | B2 |
8868619 | Raichelgauz et al. | Oct 2014 | B2 |
8880539 | Raichelgauz et al. | Nov 2014 | B2 |
8880566 | Raichelgauz et al. | Nov 2014 | B2 |
8886648 | Procopio et al. | Nov 2014 | B1 |
8898568 | Bull et al. | Nov 2014 | B2 |
8922414 | Raichelgauz et al. | Dec 2014 | B2 |
8959037 | Raichelgauz et al. | Feb 2015 | B2 |
8990125 | Raichelgauz et al. | Mar 2015 | B2 |
9009086 | Raichelgauz et al. | Apr 2015 | B2 |
9031999 | Raichelgauz et al. | May 2015 | B2 |
9087049 | Raichelgauz et al. | Jul 2015 | B2 |
9104747 | Raichelgauz et al. | Aug 2015 | B2 |
9165406 | Gray et al. | Oct 2015 | B1 |
9191626 | Raichelgauz et al. | Nov 2015 | B2 |
9197244 | Raichelgauz et al. | Nov 2015 | B2 |
9218606 | Raichelgauz et al. | Dec 2015 | B2 |
9235557 | Raichelgauz et al. | Jan 2016 | B2 |
9256668 | Raichelgauz et al. | Feb 2016 | B2 |
9323754 | Ramanathan et al. | Apr 2016 | B2 |
9330189 | Raichelgauz et al. | May 2016 | B2 |
9384196 | Raichelgauz et al. | Jul 2016 | B2 |
9438270 | Raichelgauz et al. | Sep 2016 | B2 |
9606992 | Geisner et al. | Mar 2017 | B2 |
20010019633 | Tenze et al. | Sep 2001 | A1 |
20010038876 | Anderson | Nov 2001 | A1 |
20010056427 | Yoon et al. | Dec 2001 | A1 |
20020010682 | Johnson | Jan 2002 | A1 |
20020019881 | Bokhari et al. | Feb 2002 | A1 |
20020038299 | Zernik et al. | Mar 2002 | A1 |
20020059580 | Kalker et al. | May 2002 | A1 |
20020072935 | Rowse et al. | Jun 2002 | A1 |
20020087530 | Smith et al. | Jul 2002 | A1 |
20020099870 | Miller et al. | Jul 2002 | A1 |
20020107827 | Benitez-Jimenez et al. | Aug 2002 | A1 |
20020123928 | Eldering et al. | Sep 2002 | A1 |
20020126872 | Brunk et al. | Sep 2002 | A1 |
20020129140 | Peled et al. | Sep 2002 | A1 |
20020129296 | Kwiat et al. | Sep 2002 | A1 |
20020143976 | Barker et al. | Oct 2002 | A1 |
20020147637 | Kraft et al. | Oct 2002 | A1 |
20020152267 | Lennon | Oct 2002 | A1 |
20020157116 | Jasinschi | Oct 2002 | A1 |
20020159640 | Vaithilingam et al. | Oct 2002 | A1 |
20020161739 | Oh | Oct 2002 | A1 |
20020163532 | Thomas et al. | Nov 2002 | A1 |
20020174095 | Lulich et al. | Nov 2002 | A1 |
20020178410 | Haitsma et al. | Nov 2002 | A1 |
20030028660 | Igawa et al. | Feb 2003 | A1 |
20030041047 | Chang et al. | Feb 2003 | A1 |
20030050815 | Seigel et al. | Mar 2003 | A1 |
20030078766 | Appelt et al. | Apr 2003 | A1 |
20030086627 | Berriss et al. | May 2003 | A1 |
20030089216 | Birmingham et al. | May 2003 | A1 |
20030105739 | Essafi et al. | Jun 2003 | A1 |
20030126147 | Essafi et al. | Jul 2003 | A1 |
20030182567 | Barton et al. | Sep 2003 | A1 |
20030191764 | Richards | Oct 2003 | A1 |
20030200217 | Ackerman | Oct 2003 | A1 |
20030217335 | Chung et al. | Nov 2003 | A1 |
20030229531 | Beckerman et al. | Dec 2003 | A1 |
20040003394 | Ramaswamy | Jan 2004 | A1 |
20040025180 | Begeja et al. | Feb 2004 | A1 |
20040068510 | Hayes et al. | Apr 2004 | A1 |
20040107181 | Rodden | Jun 2004 | A1 |
20040111465 | Chuang et al. | Jun 2004 | A1 |
20040117367 | Smith et al. | Jun 2004 | A1 |
20040128142 | Whitham | Jul 2004 | A1 |
20040128511 | Sun et al. | Jul 2004 | A1 |
20040133927 | Sternberg et al. | Jul 2004 | A1 |
20040153426 | Nugent | Aug 2004 | A1 |
20040215663 | Liu et al. | Oct 2004 | A1 |
20040249779 | Nauck et al. | Dec 2004 | A1 |
20040260688 | Gross | Dec 2004 | A1 |
20040267774 | Lin et al. | Dec 2004 | A1 |
20050021394 | Miedema et al. | Jan 2005 | A1 |
20050114198 | Koningstein et al. | May 2005 | A1 |
20050131884 | Gross et al. | Jun 2005 | A1 |
20050144455 | Haitsma | Jun 2005 | A1 |
20050172130 | Roberts | Aug 2005 | A1 |
20050177372 | Wang et al. | Aug 2005 | A1 |
20050238238 | Xu et al. | Oct 2005 | A1 |
20050245241 | Durand et al. | Nov 2005 | A1 |
20050262428 | Little et al. | Nov 2005 | A1 |
20050281439 | Lange | Dec 2005 | A1 |
20050289163 | Gordon et al. | Dec 2005 | A1 |
20050289590 | Cheok et al. | Dec 2005 | A1 |
20060004745 | Kuhn et al. | Jan 2006 | A1 |
20060013451 | Haitsma | Jan 2006 | A1 |
20060020860 | Tardif et al. | Jan 2006 | A1 |
20060020958 | Allamanche et al. | Jan 2006 | A1 |
20060026203 | Tan et al. | Feb 2006 | A1 |
20060031216 | Semple et al. | Feb 2006 | A1 |
20060041596 | Stirbu et al. | Feb 2006 | A1 |
20060048191 | Xiong | Mar 2006 | A1 |
20060064037 | Shalon et al. | Mar 2006 | A1 |
20060112035 | Cecchi et al. | May 2006 | A1 |
20060129822 | Snijder et al. | Jun 2006 | A1 |
20060143674 | Jones et al. | Jun 2006 | A1 |
20060153296 | Deng | Jul 2006 | A1 |
20060159442 | Kim et al. | Jul 2006 | A1 |
20060173688 | Whitham | Aug 2006 | A1 |
20060184638 | Chua et al. | Aug 2006 | A1 |
20060204035 | Guo et al. | Sep 2006 | A1 |
20060217818 | Fujiwara | Sep 2006 | A1 |
20060217828 | Hicken | Sep 2006 | A1 |
20060224529 | Kermani | Oct 2006 | A1 |
20060236343 | Chang | Oct 2006 | A1 |
20060242139 | Butterfield et al. | Oct 2006 | A1 |
20060242554 | Gerace et al. | Oct 2006 | A1 |
20060247983 | Dalli | Nov 2006 | A1 |
20060248558 | Barton et al. | Nov 2006 | A1 |
20060253423 | McLane et al. | Nov 2006 | A1 |
20070009159 | Fan | Jan 2007 | A1 |
20070011151 | Hagar et al. | Jan 2007 | A1 |
20070019864 | Koyama et al. | Jan 2007 | A1 |
20070022374 | Huang et al. | Jan 2007 | A1 |
20070033163 | Epstein et al. | Feb 2007 | A1 |
20070038608 | Chen | Feb 2007 | A1 |
20070038614 | Guha | Feb 2007 | A1 |
20070042757 | Jung et al. | Feb 2007 | A1 |
20070061302 | Ramer et al. | Mar 2007 | A1 |
20070067304 | Ives | Mar 2007 | A1 |
20070067682 | Fang | Mar 2007 | A1 |
20070071330 | Dostveen et al. | Mar 2007 | A1 |
20070074147 | Wold | Mar 2007 | A1 |
20070083611 | Farago et al. | Apr 2007 | A1 |
20070091106 | Moroney | Apr 2007 | A1 |
20070130112 | Lin | Jun 2007 | A1 |
20070130159 | Gulli et al. | Jun 2007 | A1 |
20070168413 | Barletta et al. | Jul 2007 | A1 |
20070174320 | Chou | Jul 2007 | A1 |
20070195987 | Rhoads | Aug 2007 | A1 |
20070220573 | Chiussi et al. | Sep 2007 | A1 |
20070244902 | Seide et al. | Oct 2007 | A1 |
20070253594 | Lu et al. | Nov 2007 | A1 |
20070255785 | Hayashi et al. | Nov 2007 | A1 |
20070268309 | Tanigawa et al. | Nov 2007 | A1 |
20070282826 | Hoeber et al. | Dec 2007 | A1 |
20070294295 | Finkelstein et al. | Dec 2007 | A1 |
20070298152 | Baets | Dec 2007 | A1 |
20080046406 | Seide et al. | Feb 2008 | A1 |
20080049629 | Morrill | Feb 2008 | A1 |
20080072256 | Boicey et al. | Mar 2008 | A1 |
20080091527 | Silverbrook et al. | Apr 2008 | A1 |
20080152231 | Gokturk et al. | Jun 2008 | A1 |
20080163288 | Ghosal et al. | Jul 2008 | A1 |
20080165861 | Wen et al. | Jul 2008 | A1 |
20080172615 | Igelman et al. | Jul 2008 | A1 |
20080201299 | Lehikoinen et al. | Aug 2008 | A1 |
20080204706 | Magne et al. | Aug 2008 | A1 |
20080228995 | Tan et al. | Sep 2008 | A1 |
20080237359 | Silverbrook et al. | Oct 2008 | A1 |
20080253737 | Kimura et al. | Oct 2008 | A1 |
20080263579 | Mears et al. | Oct 2008 | A1 |
20080270373 | Oostveen et al. | Oct 2008 | A1 |
20080313140 | Pereira | Dec 2008 | A1 |
20090013414 | Washington et al. | Jan 2009 | A1 |
20090022472 | Bronstein et al. | Jan 2009 | A1 |
20090024641 | Quigley et al. | Jan 2009 | A1 |
20090037408 | Rodgers | Feb 2009 | A1 |
20090043637 | Eder | Feb 2009 | A1 |
20090043818 | Raichelgauz et al. | Feb 2009 | A1 |
20090089587 | Brunk et al. | Apr 2009 | A1 |
20090119157 | Dulepet | May 2009 | A1 |
20090125544 | Brindley | May 2009 | A1 |
20090148045 | Lee et al. | Jun 2009 | A1 |
20090157575 | Schobben et al. | Jun 2009 | A1 |
20090172030 | Schiff et al. | Jul 2009 | A1 |
20090175538 | Bronstein et al. | Jul 2009 | A1 |
20090245573 | Saptharishi et al. | Oct 2009 | A1 |
20090245603 | Koruga et al. | Oct 2009 | A1 |
20090253583 | Yoganathan | Oct 2009 | A1 |
20090259687 | Mai et al. | Oct 2009 | A1 |
20090277322 | Cai et al. | Nov 2009 | A1 |
20100042646 | Raichelgauz et al. | Feb 2010 | A1 |
20100082684 | Churchill et al. | Apr 2010 | A1 |
20100104184 | Bronstein et al. | Apr 2010 | A1 |
20100125569 | Nair et al. | May 2010 | A1 |
20100162405 | Cook et al. | Jun 2010 | A1 |
20100173269 | Puri et al. | Jul 2010 | A1 |
20100268524 | Nath et al. | Oct 2010 | A1 |
20100306193 | Pereira et al. | Dec 2010 | A1 |
20100318493 | Wessling | Dec 2010 | A1 |
20100322522 | Wang et al. | Dec 2010 | A1 |
20110052063 | McAuley et al. | Mar 2011 | A1 |
20110055585 | Lee | Mar 2011 | A1 |
20110145068 | King et al. | Jun 2011 | A1 |
20110202848 | Ismalon | Aug 2011 | A1 |
20110246566 | Kashef et al. | Oct 2011 | A1 |
20110251896 | Impollonia et al. | Oct 2011 | A1 |
20110296315 | In et al. | Dec 2011 | A1 |
20110313856 | Cohen et al. | Dec 2011 | A1 |
20120082362 | Diem et al. | Apr 2012 | A1 |
20120131454 | Shah | May 2012 | A1 |
20120150890 | Jeong et al. | Jun 2012 | A1 |
20120167133 | Carroll et al. | Jun 2012 | A1 |
20120185445 | Borden et al. | Jul 2012 | A1 |
20120197857 | Huang et al. | Aug 2012 | A1 |
20120197895 | Isaacson et al. | Aug 2012 | A1 |
20120239694 | Avner et al. | Sep 2012 | A1 |
20120299961 | Ramkumar et al. | Nov 2012 | A1 |
20120330869 | Durham | Dec 2012 | A1 |
20120331011 | Raichelgauz et al. | Dec 2012 | A1 |
20130031489 | Gubin et al. | Jan 2013 | A1 |
20130066856 | Ong et al. | Mar 2013 | A1 |
20130067035 | Amanat et al. | Mar 2013 | A1 |
20130067364 | Bemtson et al. | Mar 2013 | A1 |
20130080433 | Raichelgauz et al. | Mar 2013 | A1 |
20130086499 | Dyor et al. | Apr 2013 | A1 |
20130089248 | Remiszewski et al. | Apr 2013 | A1 |
20130104251 | Moore et al. | Apr 2013 | A1 |
20130159298 | Mason et al. | Jun 2013 | A1 |
20130173257 | Rose et al. | Jul 2013 | A1 |
20130173635 | Sanjeev | Jul 2013 | A1 |
20130179514 | Arora et al. | Jul 2013 | A1 |
20130197938 | Bayouk et al. | Aug 2013 | A1 |
20130226930 | Amgren et al. | Aug 2013 | A1 |
20130232157 | Kamel | Sep 2013 | A1 |
20130311924 | Denker et al. | Nov 2013 | A1 |
20130325550 | Varghese et al. | Dec 2013 | A1 |
20130332951 | Gharaat et al. | Dec 2013 | A1 |
20140019264 | Wachman et al. | Jan 2014 | A1 |
20140025692 | Pappas | Jan 2014 | A1 |
20140147829 | Jerauld | May 2014 | A1 |
20140152698 | Kim et al. | Jun 2014 | A1 |
20140176604 | Venkitaraman et al. | Jun 2014 | A1 |
20140188786 | Raichelgauz et al. | Jul 2014 | A1 |
20140193077 | Shiiyama et al. | Jul 2014 | A1 |
20140250032 | Huang et al. | Sep 2014 | A1 |
20140282655 | Roberts | Sep 2014 | A1 |
20140300722 | Garcia | Oct 2014 | A1 |
20140310825 | Raichelgauz et al. | Oct 2014 | A1 |
20140330830 | Raichelgauz et al. | Nov 2014 | A1 |
20150154189 | Raichelgauz et al. | Jun 2015 | A1 |
20150286742 | Zhang et al. | Oct 2015 | A1 |
20150289022 | Gross | Oct 2015 | A1 |
20160026707 | Ong et al. | Jan 2016 | A1 |
20160239566 | Raichelgauz et al. | Aug 2016 | A1 |
Number | Date | Country |
---|---|---|
0231764 | Apr 2002 | WO |
2003005242 | Jan 2003 | WO |
2003067467 | Aug 2003 | WO |
2004019527 | Mar 2004 | WO |
2005027457 | Mar 2005 | WO |
20070049282 | May 2007 | WO |
2014137337 | Sep 2014 | WO |
2016040376 | Mar 2016 | WO |
Entry |
---|
Brecheisen, et al., “Hierarchical Genre Classification for Large Music Collections”, ICME 2006, pp. 1385-1388. |
Lau, et al., “Semantic Web Service Adaptation Model for a Pervasive Learning Scenario”, 2008 IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications Year: 2008, pp. 98-103, DOI: 10.1109/CITISIA.2008.4607342 IEEE Conference Publications. |
McNamara, et al., “Diversity Decay in Opportunistic Content Sharing Systems”, 2011 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks Year: 2011, pp. 1-3, DOI: 10.1109/WoWMoM.2011.5986211 IEEE Conference Publications. |
Santos, et al., “SCORM-MPEG: an Ontology of Interoperable Metadata for Multimedia and e-Learning”, 2015 23rd International Conference on Software, Telecommunications and Computer Networks (SoftCOM) Year 2015, pp. 224-228, DOI: 10.1109/SOFTCOM.2015.7314122 IEEE Conference Publications. |
Wilk, et al., “The Potential of Social-Aware Multimedia Prefetching on Mobile Devices”, 2015 International Conference and Workshops on Networked Systems (NetSys) Year: 2015, pp. 1-5, DOI: 10.1109/NetSys.2015.7089081 IEEE Conference Publications. |
Liu, et al., “Instant Mobile Video Search With Layered Audio-Video Indexing and Progressive Transmission”, Multimedia, IEEE Transactions on Year: 2014, vol. 16, Issue: 8, pp. 2242-2255, DOI: 10.1109/TMM.2014.2359332 IEEE Journals & Magazines. |
Mladenovic, et al., “Electronic Tour Guide for Android Mobile Platform with Multimedia Travel Book”, Telecommunications Forum (TELFOR), 2012 20th Year: 2012, pp. 1460-1463, DOI: 10.1109/TELFOR.2012.6419494 IEEE Conference Publication&. |
Park, et al., “Compact Video Signatures for Near-Duplicate Detection on Mobile Devices”, Consumer Electronics (ISCE 2014), The 18th IEEE International Symposium on Year: 2014, pp. 1-2, DOI: 10.1109/ISCE.2014.6884293 IEEE Conference Publications. |
Wang et al. “A Signature for Content-based Image Retrieval Using a Geometrical Transform”, ACM 1998, pp. 229-234. |
Zang, et al., “A New Multimedia Message Customizing Framework for Mobile Devices”, Multimedia and Expo, 2007 IEEE International Conference on Year: 2007, pp. 1043-1046, DOI: 10.1109/ICME.2007.4284832 IEEE Conference Publications. |
Boari et al, “Adaptive Routing for Dynamic Applications in Massively Parallel Architectures”, 1995 IEEE, Spring 1995. |
Burgsteiner et al.: “Movement Prediction From Real-World Images Using a Liquid State Machine”, Innovations in Applied Artificial Intelligence Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence, LNCS, Springer-Verlag, BE, vol. 3533, Jun. 2005, pp. 121-130. |
Cernansky et al., “Feed-forward Echo State Networks”; Proceedings of International Joint Conference on Neural Networks, Montreal, Canada, Jul. 31-Aug. 4, 2005. |
Cococcioni, et al, “Automatic Diagnosis of Defects of Rolling Element Bearings Based on Computational Intelligence Techniques”, University of Pisa, Pisa, Italy, 2009. |
Emami, et al, “Role of Spatiotemporal Oriented Energy Features for Robust Visual Tracking in Video Surveillance, University of Queensland”, St. Lucia, Australia, 2012. |
Fathy et al., “A Parallel Design and Implementation For Backpropagation Neural Network Using NIMD Architecture”, 8th Mediterranean Electrotechnical Corsfe rersce, 19'96. MELECON '96, Date of Conference: May 13-16, 1996, vol. 3, pp. 1472-1475. |
Foote, Jonathan, et al. “Content-Based Retrieval of Music and Audio”, 1997 Institute of Systems Science, National University of Singapore, Singapore (Abstract). |
Freisleben et al., “Recognition of Fractal Images Using a Neural Network”, Lecture Notes in Computer Science, 1993, vol. 6861, 1993, pp. 631-637. |
Garcia, “Solving the Weighted Region Least Cost Path Problem Using Transputers”, Naval Postgraduate School, Monterey, California, Dec. 1989. |
Guo et al, “AdOn: An Intelligent Overlay Video Advertising System”, SIGIR, Boston, Massachusetts, Jul. 19-23, 2009. |
Howlett et al., “A Multi-Computer Neural Network Architecture in a Virtual Sensor System Application”, International Journal of Knowledge-based Intelligent Engineering Systems, 4 (2). pp. 86-93, 133N 1327-2314; first submitted Nov. 30, 1999; revised version submitted Mar. 10, 2000. |
International Search Authority: “Written Opinion of the International Searching Authority” (PCT Rule 43bis.1) including International Search Report for International Patent Application No. PCT/US2008/073852; dated Jan. 28, 2009. |
International Search Authority: International Preliminary Report on Patentability (Chapter I of the Patent Cooperation Treaty) including “Written Opinion of the International Searching Authority” (PCT Rule 43bis. 1) for the corresponding International Patent Application No. PCT/IL2006/001235; dated Jul. 28, 2009. |
International Search Report for the corresponding International Patent Application PCT/IL2006/001235; dated Nov. 2, 2008. |
IPO Examination Report under Section 18(3) for corresponding UK application No. GB1001219.3, dated Sep. 12, 2011. |
Iwamoto, K.; Kasutani, E.; Yamada, A.: “Image Signature Robust to Caption Superimposition for Video Sequence Identification”; 2006 IEEE International Conference on Image Processing; pp. 3185-3188, Oct. 8-11, 2006; doi: 10.1109/ICIP.2006.313046. |
Jaeger, H.: “The “echo state” approach to analysing and training recurrent neural networks”, GMD Report, No. 148, 2001, pp. 1-43, XP002466251 German National Research Center for Information Technology. |
Lin, C.; Chang, S.: “Generating Robust Digital Signature for Image/Video Authentication”, Multimedia and Security Workshop at ACM Mutlimedia '98; Bristol, U.K., Sep. 1998; pp. 49-54. |
Lyon, Richard F.; “Computational Models of Neural Auditory Processing”; IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP '84, Date of Conference: Mar. 1984, vol. 9, pp. 41-44. |
Maass, W. et al.: “Computational Models for Generic Cortical Microcircuits”, Institute for Theoretical Computer Science, Technische Universitaet Graz, Graz, Austria, published Jun. 10, 2003. |
Mandhaoui, et al, “Emotional Speech Characterization Based on Multi-Features Fusion for Face-to-Face Interaction”, Universite Pierre et Marie Curie, Paris, France, 2009. |
Marti et al,“Real Time Speaker Localization and Detection System for Camera Steering in Multiparticipant Videoconferencing Environments”, Universidad Politecnica de Valencia, Spain, 2011. |
Mei, et al., “Contextual In-Image Advertising”, Microsoft Research Asia, pp. 439-448, 2008. |
Mei, et al., “VideoSense—Towards Effective Online Video Advertising”, Microsoft Research Asia, pp. 1075-1084, 2007. |
Morad, T.Y. et al.: “Performance, Power Efficiency and Scalability of Asymmetric Cluster Chip Multiprocessors”, Computer Architecture Letters, vol. 4, Jul. 4, 2005 (Jul. 4, 2005), pp. 1-4, XP002466254. |
Nagy et al, “A Transputer, Based, Flexible, Real-Time Control System for Robotic Manipulators”, UKACC International Conference on Control '96, Sep. 2-5, 1996, Conference 1996, Conference Publication No. 427, IEE 1996. |
Natsclager, T. et al.: “The “liquid computer”: A novel strategy for real-time computing on time series”, Special Issue on Foundations of Information Processing of Telematik, vol. 8, No. 1, 2002, pp. 39-43, XP002466253. |
Ortiz-Boyer et al., “CIXL2: A Crossover Operator for Evolutionary Algorithms Based on Population Features”, Journal of Artificial Intelligence Research 24 (2005) 1-48 Submitted Nov. 2004; published Jul. 2005. |
Raichelgauz, I. et al.: “Co-evolutionary Learning in Liquid Architectures”, Lecture Notes in Computer Science, [Online] vol. 3512, Jun. 21, 2005 (Jun. 21, 2005), pp. 241-248, XP019010280 Springer Berlin / Heidelberg ISSN: 1611-3349 ISBN: 978-3-540-26208-4. |
Ribert et al. “An Incremental Hierarchical Clustering”, Visicon Interface 1999, pp. 586-591. |
Scheper et al, “Nonlinear dynamics in neural computation”, ESANN'2006 proceedings—European Symposium on Artificial Neural Networks, Bruges (Belgium), Apr. 26-28, 2006, d-side publi, ISBN 2-930307-06-4. |
Semizarov et al. “Specificity of Short Interfering RNA Determined through Gene Expression Signatures”, PNAS, 2003, pp. 6347-6352. |
Theodoropoulos et al, “Simulating Asynchronous Architectures on Transputer Networks”, Proceedings of the Fourth Euromicro Workshop On Parallel and Distributed Processing, 1996. PDP '96. |
Verstraeten et al., “Isolated word recognition with the Liquid State Machine: a case study”; Department of Electronics and Information Systems, Ghent University, Sint-Pietersnieuwstraat 41, 9000 Gent, Belgium, Available online Jul. 14, 2005. |
Verstraeten et al.: “Isolated word recognition with the Liquid State Machine: a case study”, Information Processing Letters, Amsterdam, NL, vol. 95, No. 6, Sep. 30, 2005 (Sep. 30, 2005), pp. 521-528, XP005028093 ISSN: 0020-0190. |
Ware et al., “Locating and Identifying Components in a Robot's Workspace using a Hybrid Computer Architecture”; Proceedings of the 1995 IEEE International Symposium on Intelligent Control, Aug. 27-29, 1995, pp. 139-144. |
Xian-Sheng Hua et al.: “Robust Video Signature Based on Ordinal Measure” In: 2004 International Conference on Image Processing, ICIP '04; Microsoft Research Asia, Beijing, China; published Oct. 24-27, 2004, pp. 685-688. |
Zeevi, Y. et al.: “Natural Signal Classification by Neural Cliques and Phase-Locked Attractors”, IEEE World Congress on Computational Intelligence, IJCNN2006, Vancouver, Canada, Jul. 2006 (Jul. 2006), XP002466252. |
Zhou et al., “Ensembling neural networks: Many could be better than all”; National Laboratory for Novel Software Technology, Nanjing Unviersirty, Hankou Road 22, Nanjing 210093, PR China; Received Nov. 16, 2001, Available online Mar. 12, 2002. |
Zhou et al., “Medical Diagnosis With C4.5 Rule Preceded by Artificial Neural Network Ensemble”; IEEE Transactions on Information Technology in Biomedicine, vol. 7, Issue: 1, pp. 37-42, Date of Publication: Mar. 2003. |
Johnson, John L., “Pulse-Coupled Neural Nets: Translation, Rotation, Scale, Distortion, and Intensity Signal Invariance for Images.” Applied Optics, vol. 33, No. 26, 1994, pp. 6239-6253. |
The International Search Report and the Written Opinion for PCT/US2016/050471, ISA/RU, Moscow, RU, dated May 4, 2017. |
The International Search Report and the Written Opinion for PCT/US2017/015831, ISA/RU, Moscow, Russia, dated Apr. 20, 2017. |
Hua, et al., “Robust Video Signature Based on Ordinal Measure”, Image Processing, 2004. 2004 International Conference on Image Processing (ICIP), vol. 1, IEEE, pp. 685-688, 2004. |
Dueluz, “Content-Based Integrity Protection of Digital Images”, SPIE Conf. on Security and Watermarking of Multimedia Contents, San Jose, Jan. 1999, pp. 85-93, downloaded from http://proceedings.spiedigitallibrary.org/ on Aug. 2, 2017. |
Schneider, et. al., “A Robust Content Based Digital Signature for Image Authentication”, Proc. ICIP 1996, Laussane, Switzerland, Oct. 1996, pp. 227-230. |
Yanagawa, et al., “Columbia University's Baseline Detectors for 374 LSCOM Semantic Visual Concepts.” Columbia University ADVENT technical report, 2007, pp. 222-2006-8. |
Zou, et al., “A Content-Based Image Authentication System with Lossless Data Hiding”, ICME 2003, pp. 213-216. |
Li, et al., “Matching Commercial Clips from TV Streams Using a Unique, Robust and Compact Signature,” Proceedings of the Digital Imaging Computing: Techniques and Applications, Feb. 2005, vol. 0-7695-2467, Australia. |
May et al., “The Transputer”, Springer-Verlag, Berlin Heidelberg, 1989, teaches multiprocessing system. |
Nam, et al., “Audio Visual Content-Based Violent Scene Characterization”, Department of Electrical and Computer Engineering, Minneapolis, MN, 1998, pp. 353-357. |
Vailaya, et al., “Content-Based Hierarchical Classification of Vacation Images,” I.E.E.E.: Multimedia Computing and Systems, vol. 1, 1999, East Lansing, MI, pp. 518-523. |
Vallet, et al., “Personalized Content Retrieval in Context Using Ontological Knowledge,” IEEE Transactions on circuits and Systems for Video Technology, vol. 17, No. 3, Mar. 2007, pp. 336-346. |
Whitby-Strevens, “The Transputer”, 1985 IEEE, Bristol, UK. |
Yanai, “Generic Image Classification Using Visual Knowledge on the Web,” MM'03, Nov. 2-8, 2003, Tokyo, Japan, pp. 167-176. |
Odinaev, et al., “Cliques in Neural Ensembles as Perception Carriers”, Technion—Israel Institute of Technology, 2006 International Joint Conference on Neural Networks, Canada, 2006, pp. 285-292. |
The International Search Report and the Written Opinion for PCT/US2016/054634 dated Mar. 16, 2017, ISA/RU, Moscow, RU. |
Clement, et al. “Speaker Diarization of Heterogeneous Web Video Files: A Preliminary Study”, Acoustics, Speech and Signal Processing (ICASSP), 2011, IEEE International Conference on Year: 2011, pp. 4432-4435, DOI: 10.1109/ICASSP.2011.5947337 IEEE Conference Publications, France. |
Gong, et al., “A Knowledge-based Mediator for Dynamic Integration of Heterogeneous Multimedia Information Sources”, Video and Speech Processing, 2004, Proceedings of 2004 International Symposium on Year: 2004, pp. 167-470, DOI: 10.1109/ISIMP.2004.1434102 IEEE Conference Publications, Hong Kong. |
Lin, et al., “Robust Digital Signature for Multimedia Authentication: A Summary”, IEEE Circuits and Systems Magazine, 4th Quarter 2003, pp. 23-26. |
Lin, et al., “Summarization of Large Scale Social Network Activity”, Acoustics, Speech and Signal Processing, 2009, ICASSP 2009, IEEE International Conference on Year 2009, pp. 3481-3484, DOI: 10.1109/ICASSP.2009.4960375, IEEE Conference Publications, Arizona. |
Nouza, et al., “Large-scale Processing, Indexing and Search System for Czech Audio-Visual Heritage Archives”, Multimedia Signal Processing (MMSP), 2012, pp. 337-342, IEEE 14th Intl. Workshop, DOI: 10.1109/MMSP.2012.6343465, Czech Republic. |
Chuan-Yu Cho, et al., “Efficient Motion-Vector-Based Video Search Using Query by Clip”, 2004, IEEE, Taiwan, pp. 1-4. |
Gomes et al., “Audio Watermaking and Fingerprinting: For Which Applications?” University of Rene Descartes, Paris, France, 2003. |
Ihab Al Kabary, et al., “SportSense: Using Motion Queries to Find Scenes in Sports Videos”, Oct. 2013, ACM, Switzerland, pp. 1-3. |
Jianping Fan et al., “Concept-Oriented Indexing of Video Databases: Towards Semantic Sensitive Retrieval and Browsing”, IEEE, vol. 13, No. 7, Jul. 2004, pp. 1-19. |
Shih-Fu Chang, et al., “VideoQ: A Fully Automated Video Retrieval System Using Motion Sketches”, 1998, IEEE, , New York, pp. 1-2. |
Wei-Te Li et al., “Exploring Visual and Motion Saliency for Automatic Video Object Extraction”, IEEE, vol. 22, No. 7, Jul. 2013, pp. 1-11. |
Zhu et al., Technology-Assisted Dietary Assessment. Computational Imaging VI, edited by Charles A. Bouman, Eric L. Miller, Ilya Pollak, Proc. of SPIE—IS&T Electronic Imaging, SPIE vol. 6814, 681411, Copyright 2008 SPIE—IS&T. pp. 1-10. |
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