US20160300145A1 - System and method for identification of deviations from periodic behavior patterns in multimedia content - Google Patents

System and method for identification of deviations from periodic behavior patterns in multimedia content Download PDF

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US20160300145A1
US20160300145A1 US15189386 US201615189386A US2016300145A1 US 20160300145 A1 US20160300145 A1 US 20160300145A1 US 15189386 US15189386 US 15189386 US 201615189386 A US201615189386 A US 201615189386A US 2016300145 A1 US2016300145 A1 US 2016300145A1
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multimedia content
signature
behavior pattern
periodic behavior
system
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Igal RAICHELGAUZ
Karina ODINAEV
Yehoshua Y. Zeevi
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Cortica Ltd
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Cortica Ltd
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    • H04H60/35Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users
    • H04H60/37Arrangements for identifying or recognising characteristics with a direct linkage to broadcast information or to broadcast space-time, e.g. for identifying broadcast stations or for identifying users for identifying segments of broadcast information, e.g. scenes or extracting programme ID
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Abstract

A method and system for identification of a deviation from a periodic behavior pattern in a sequence of multimedia content segments are provided. The method includes generating at least one signature for each multimedia content segment of the sequence of multimedia content segments; comparing at least two of the generated signatures to detect a periodic behavior pattern, wherein the compared at least two signatures represent consecutive multimedia content segments; and comparing at least one signature of at least a subsequently received multimedia content segment to at least one signature representing the periodic behavior pattern to identify a deviation from the periodic behavior pattern.

Description

    CROSS-REFERENCE TO RELATED APPLICATIONS
  • This application is a continuation of U.S. patent application Ser. No. 14/509,543 filed on Oct. 8, 2014, now allowed, which claims the benefit of U.S. Provisional Application No. 61/889,542 filed on Oct. 11, 2013. The Ser. No. 14/509,543 application is also a continuation-in-part (CIP) of U.S. patent application Ser. No. 13/770,603 filed on Feb. 19, 2013, now pending. The Ser. No. 13/770,603 application is a CIP of U.S. patent application Ser. No. 13/624,397 filed on Sep. 21, 2012, now U.S. Pat. No. 9,191,626. The Ser. No. 13/624,397 is a CIP of:
  • (a) U.S. patent application Ser. No. 13/344,400 filed on Jan. 5, 2012, now U.S. Pat. No. 8,959,037, which is a continuation of U.S. patent application Ser. No. 12/434,221, filed May 01, 2009, now U.S. Pat. No. 8,112,376;
  • (b) U.S. patent application Ser. No. 12/195,863, filed 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 below-referenced U.S. patent application Ser. No. 12/084,150; and
  • (c) 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.
  • All of the applications referenced above are herein incorporated by reference for all that they contain.
  • TECHNICAL FIELD
  • The present invention relates generally to the analysis of multimedia content, and more specifically to a system and method for detecting common patterns in multimedia content and determining deviation from the identified common patterns.
  • BACKGROUND
  • Multimedia capturing devices are commonly used for monitoring different areas or activities such as traffic control, security control, and human health diagnosis. For example, multimedia capturing devices may be utilized for monitoring babies' breathing while they sleep based on sounds made by the babies during sleep.
  • The multimedia capturing devices may implement or utilize content recognition solutions for analyzing the different types of multimedia content. Such solutions are designed to process, analyze, and understand multimedia content from the real world in order to produce numerical or symbolic information to reach certain decisions. A decision in multimedia content analysis may be, for example, a detection of an irregular pattern throughout the multimedia content.
  • Various techniques for pattern recognition are disclosed in the related art. However, due to the fact that patterns are often evenly distributed within the data, recognition of uncommon patterns typically requires extensive computing resources. Specifically, some patterns can be more prominent than others. Such patterns are likely to have a larger number of occurrences, while other patterns may be very rare. In addition, some patterns may be correlated to each other, and together such patterns form pattern-combinations which may also be very popular. This poses a problem to applications for pattern recognition systems.
  • As pattern recognition is not an easy problem to solve, detection of deviations from such patterns also poses similar challenges as well. Therefore, the ability to identify irregular events by analysis of multimedia content may be limited.
  • SUMMARY
  • The various disclosed embodiments include a method for identification of a deviation from a periodic behavior pattern in a sequence of multimedia content segments. The method comprises generating at least one signature for each multimedia content segment of the sequence of multimedia content segments; comparing at least two of the generated signatures to detect a periodic behavior pattern, wherein the compared at least two signatures represent consecutive multimedia content segments; and comparing at least one signature of at least a subsequently received multimedia content segment to at least one signature representing the periodic behavior pattern to identify a deviation from the periodic behavior pattern.
  • The various disclosed embodiments include a system for a system for identification of a deviation from a periodic behavior pattern in a sequence of multimedia content segments. The system comprises a processing system; and a memory connected to the processing system, the memory containing instructions that, when executed by the processing system, configure the system to: generate at least one signature for each multimedia content segment of the sequence of multimedia content segments; compare at least two of the generated signatures to detect a periodic behavior pattern, wherein the compared at least two signatures represent consecutive multimedia content segments; and compare at least one signature of at least a subsequently received multimedia content segment to at least one signature representing the periodic behavior pattern to identify a deviation from the periodic behavior pattern.
  • BRIEF DESCRIPTION OF THE DRAWINGS
  • The subject matter disclosed herein 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.
  • FIG. 1 is a schematic block diagram of a network system utilized to describe the various embodiments disclosed herein;
  • FIG. 2 is a flowchart describing a method of identifying deviations from common patterns in multimedia content according to an embodiment;
  • FIG. 3 is a block diagram depicting the basic flow of information in the signature generator system according to an embodiment; and
  • FIG. 4 is a diagram showing the flow of patches generation, response vector generation, and signature generation in a large-scale speech-to-text system according to an embodiment.
  • DETAILED DESCRIPTION
  • 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.
  • By way of example, the disclosed embodiments include a method and system for identification of deviations from common patterns in multimedia content. The identification of deviations is based on analysis of a sequence of segments of continuously received multimedia content. The analysis is based on signatures generated for the segments of content and detection of periodic behavior pattern of the signatures. In an embodiment, upon identification of a deviation from the detected periodic behavior pattern, a notification is provided to a user.
  • FIG. 1 shows an exemplary and non-limiting schematic diagram of a network system 100 utilized to describe the various embodiments disclosed herein. A network 110 is used to communicate between different elements of the system 100. The network 110 may be the Internet, the world-wide-web (WWW), a local area network (LAN), a wide area network (WAN), a metro area network (MAN), and other networks capable of enabling communication between the elements of the system 100.
  • Further connected to the network 110 are one or more computing devices (CDs) 120-1 through 120-n (hereinafter referred to collectively as computing device 120 or individually as a computing device 120). A computing device 120 may be, for example, a personal computer (PC), a personal digital assistant (PDA), a mobile phone, a wearable computing device, a smart phone, a tablet computer, a camera (e.g., surveillance camera, a traffic control camera, etc.), and other kinds of wired and mobile appliances, equipped with capabilities such as capturing, storing, and managing capabilities, that are enabled as further discussed herein below. The computing device 120 may further comprise an application software 125 (hereinafter referred to as an application or app, merely for simplicity purposes) installed therein. The application 125 is configured to provide continuous segments of multimedia content captured by or stored in the respective computing device 120 to a server 130. The application 125 is also configured to receive notifications from the server 130 about irregular events being detected. In an exemplary embodiment, the applications 125 are programmed for security, traffic control, entertainment, and other such purposes. An application 125 may be installed in the respective computing device or downloaded from an application repository, such as the AppStore®, Google Play® and the like.
  • The various embodiments disclosed herein are realized using the server 130 and a signature generator system (SGS) 140. The SGS 140 may be connected to the server 130 directly or through the network 110. The server 130 is configured to receive and serve multimedia content that comprises a sequence of segments and to cause the SGS 140 to generate a signature respective of each segment within the multimedia content. The process for generating the signatures for the multimedia content segments is explained in more detail herein below with respect to FIGS. 3 and 4. A multimedia content segment may be, for example, an image, a graphic, a video signal, an audio signal, a photograph, and an image of signals (e.g., spectrograms, phasograms, scalograms, etc.), and/or combinations thereof and portions thereof.
  • According to the disclosed embodiments, the server 130 is configured to analyze the signatures generated for the content segments to detect periodic behaviors patterns. In a non-limiting embodiment, a periodic behavior pattern may be related to an object that appears and/or an event or activity that occurs through a number of consecutive segments with regular time intervals. In an embodiment, the detection of periodic behavior patterns is achieved by comparing signatures of consecutive content segments to each other. At least two signatures of at least two different content segments are considered as having a periodic behavior pattern if the respective signatures overlap more than a preconfigured threshold level. The preconfigured threshold level may be configured based on, for example, the sensitivity of the detection. For example, a lower threshold value may be set for a security application than would be set for an entertainment application.
  • In an exemplary embodiment, a decision threshold value is also preconfigured. The decision threshold value is the number of consecutive content segments of matching signatures. The decision threshold value may be based on, but is not limited to, an actual number of segments or a time duration. That is, a common behavior pattern is detected only if the same object or event is identified in a number of consecutive content segments or in consecutive content segments of a total time duration exceeding the decision threshold.
  • In an exemplary embodiment, a baseline signature representing a common periodic behavior pattern is generated and utilized for comparison with signatures other content segments. Such a baseline signature may be generated by correlating signatures of content segments determined to present a periodic behavior pattern.
  • The server 130 is further configured to detect at least one deviation from the identified periodic behavior pattern. This can be achieved by determining that one or more signatures generated for content segments do not match the baseline signature and/or that at least one signature generated for a segment represents a behavior pattern. Thus, in an embodiment, a deviation from the identified periodic behavior pattern is realized by comparing the respective signatures. At least two different content segments are considered as deviated if the respective signatures overlap less than a preconfigured threshold level. It should be noted that the same threshold level used for the detection of periodic behavior patterns may be utilized to detect the deviations. In another embodiment, different threshold levels are utilized for the identification of behavior patterns and deviations from such patterns.
  • In an embodiment, upon identification of such deviation, a notification is generated by the server 130 and provided to the computing device 120 or to another device connected to the server 130 through the network 110 as predetermined by the computing device 120.
  • As noted above, a periodic behavior pattern may be related to an object that appears and/or an event that occurs through a number of consecutive content segments. In an embodiment, in order to identify such objects and/or events, the server 130 is configured to determine the context of the content segments.
  • A context is a set of common patterns among concepts. Mathematically, a context can be represented as a matrix of co-occurrences of concepts. A threshold may be associated with the number of co-occurrences of concepts in the matrix to establish a context. A concept is a collection of signatures representing a multimedia element and metadata describing the concept. The collection is a signature reduced cluster generated by inter-matching the signatures generated for the many multimedia elements, clustering the inter-matched signatures, and providing a reduced cluster set of such clusters. 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 consisting of textual representations of the Superman concept. Techniques for generating concepts and concept structures are described further in the 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.
  • As an example, a content segment (or a set of more than one content segment) may include images of palm trees, a beach, and the coast line of San Diego. In this example, the determined context of the content segment may be determined to be “California sea shore.”
  • In certain implementations, one or more probabilistic models may be utilized to determine the correlation between signatures representing concepts in order to determine the context. The probabilistic models determine, for example, the probability that a signature may appear in the same orientation and in the same ratio as another signature.
  • Alternatively or collectively, according to another embodiment, the server 130 is further configured to utilize one or more environmental variables related to the sequence of content segments to determine the context. Such environmental variables may include, for example, a time of the day, a capturing place, and so on.
  • As an example, the server 130 is configured to receive a video from a traffic control camera of a 3 mile long section of a highway. The video is captured during rush hour time. The server 130 together with the SGS 140 identifies that every morning around 200 vehicles are driving in the highway at an average speed of 50 miles per hour. Upon identification of a deviation from this periodic behavior pattern, for example in a case where 20 vehicles are driving in an average speed of 20 miles per hour on the section of the highway, a notification is provided by the server 130. As another example, where only around 100 vehicles are driving on the highway, a deviation may be identified.
  • The server 130 further communicates with a data warehouse 150 through the network 110. In other non-limiting configurations (not shown), the server 130 is directly connected to the data warehouse 150. The data warehouse 150 is configured to store the multimedia content, periodic behaviors within the multimedia content, and deviations from periodic behaviors as determined by the server 130.
  • It should be noted that each of the server 130 and the SGS 140 typically comprises a processing system (not shown) that is coupled to a memory (not shown), and optionally a network interface (not shown). The processing system is connected to the memory, which typically contains instructions that can be executed by the processing system. The server 130 may also include a network interface (not shown) to the network 110. In one embodiment, the processing system is realized or includes an array of Computational Cores configured as discussed in more detail below. In another embodiment, the processing system of each of the server 130 and SGS 140 may comprise or be a component of a larger processing system implemented with one or more processors. The one or more processors may be implemented with any combination of general-purpose microprocessors, microcontrollers, digital signal processors (DSPs), field programmable gate array (FPGAs), programmable logic devices (PLDs), controllers, state machines, gated logic, discrete hardware components, dedicated hardware finite state machines, or any other suitable entities that can perform calculations or other manipulations of information.
  • FIG. 2 depicts an exemplary and non-limiting flowchart 200 describing the process of identifying deviations from behavior patterns in multimedia content according to one embodiment. At S210, a sequence of segments of multimedia content is received from a computing device such as, for example, the computing device 120-1.
  • In S220, at least one signature is generated for each segment in the multimedia content. The signatures for each segment in the multimedia content are generated by the SGS 140 as further described herein below with respect to FIGS. 3 and 4. The at least one generated signature is robust to noise and distortion.
  • In S230, a periodic behavior pattern is detected through the received consecutive content segments. The detection is based on the generated signatures. According to one embodiment, a first threshold level and the decision threshold are set to preconfigured values. The signatures of each of the consecutive content segments are matched to each other. At least two signatures of the two different content segments are considered as having a periodic behavior pattern when the respective signatures overlap more than the value of the threshold level. A detection of a periodic behavior pattern is achieved when the number of matching consecutive segments exceeds the decision threshold. At S235, it is checked if a periodic behavior pattern is detected, and if so, execution continues with S240; otherwise, execution continues with S260. In an embodiment, if a periodic behavior pattern is identified, a baseline signature is generated respective thereof.
  • In S240, a deviation from the periodic behavior pattern is detected. In an embodiment, S240 includes comparing signatures generated for content segments to the baseline signature and/or at least one signature generated for a segment that represents a behavior pattern. In case at least two compared signatures do not overlap less than a second preconfigured threshold level, a deviation is detected. In an embodiment, the first and second threshold levels are the same.
  • In S245, it is checked whether at least one deviation from the at least one periodic behavior is identified and, if so, execution continues with S250, where a notification respective of the deviation is generated and reported; otherwise, execution continues with S260. In S260, it is checked whether operation will continue and, if so, execution continues with S220; otherwise, execution terminates.
  • FIGS. 3 and 4 illustrate the generation of signatures for the multimedia content elements by the SGS 140 according to one embodiment. An exemplary high-level description of the process for large scale matching is depicted in FIG. 3. In this example, the matching is for a video content.
  • 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 FIG. 3. Finally, Target Robust Signatures and/or Signatures are effectively matched, by a matching algorithm 9, to Master Robust Signatures and/or Signatures database to find all matches between the two databases.
  • To demonstrate an example of the 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 is now described with reference to FIG. 4. The first step in the process of signatures generation from a given speech-segment is to breakdown the speech-segment to K patches 14 of random length P and random position within the speech segment 12. The breakdown is performed by the patch generator component 21. The value of the number of patches K, random length P and random position parameters is determined based on optimization, considering the tradeoff between accuracy rate and the number of fast matches required in the flow process of the server 130 and SGS 140. Thereafter, all the K patches are injected in parallel into all computational Cores 3 to generate K response vectors 22, which are fed into a signature generator system 23 to produce a database of Robust Signatures and Signatures 4.
  • 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 ‘i’ is injected into all the Cores 3. Then, Cores 3 generate two binary response vectors: {right arrow over (S)} which is a Signature vector, and {right arrow over (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:
  • V i = j w ij k j n i = ( Vi - Th x )
  • where,
    Figure US20160300145A1-20161013-P00001
    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 ‘j’ (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:
  • 1: For: Vi>ThRS

  • 1−p(V>Th S)−1−(1−ε)l<<1
  • i.e., given that l 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).
  • 2: p(Vi>ThRS)≈l/L
  • i.e., approximately l out of the total L nodes can be found to generate a Robust Signature according to the above definition.
  • 3: Both Robust Signature and Signature are generated for certain frame i.
  • 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. The detailed description of the Signature generation can be found in 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.
  • A detailed description of the Computational Core generation and the process for configuring such cores is discussed in more detail in the co-pending U.S. Pat. No. 8,655,801 referenced above.
  • 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 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.

Claims (19)

    What is claimed is:
  1. 1. A method for identification of a deviation from a periodic behavior pattern in a sequence of multimedia content segments, comprising:
    generating at least one signature for each multimedia content segment of the sequence of multimedia content segments;
    comparing at least two of the generated signatures to detect a periodic behavior pattern, wherein the compared at least two signatures represent consecutive multimedia content segments; and
    comparing at least one signature of at least a subsequently received multimedia content segment to at least one signature representing the periodic behavior pattern to identify a deviation from the periodic behavior pattern.
  2. 2. The method of claim of 1, wherein each multimedia content segment of the sequence of multimedia content segments is at least one of: an image, a graphic, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, images of signals, and a portion thereof.
  3. 3. The method of claim 1, wherein the periodic behavior pattern is related to at least one of: an object that appears in the sequence of multimedia content segments at regular time intervals, an event that occurs through the sequence of multimedia content segments at regular time intervals, and an activity that occurs through a number of consecutive segments at regular time intervals.
  4. 4. The method of claim 3, wherein the object, the event, and the activity are determined using a context of the received sequence of multimedia content segments.
  5. 5. The method of claim 1, wherein the periodic behavior pattern is detected upon identification of a number of matching signatures of consecutive content segments exceeding a first threshold.
  6. 6. The method of claim 5, wherein at least two signatures match when the at least two signatures overlap more than a first matching threshold level.
  7. 7. The method of claim 1, further comprising:
    generating a baseline signature based on the periodic behavior pattern, wherein identifying the deviation from the periodic behavior pattern further includes comparing the at least one signature of the at least a subsequently received multimedia content segment to the baseline signature.
  8. 8. The method of claim 1, further comprising:
    identifying a multimedia content segment representing the periodic behavior pattern; and
    generating a representative segment signature based on the identified multimedia content segment representing the periodic behavior pattern, wherein identifying the deviation from the periodic behavior pattern further includes comparing the at least one signature of the at least a subsequently received multimedia content segment to the representative segment signature.
  9. 9. The method of claim 1, wherein each signature is generated by a signature generator system, wherein the signature generator system includes a plurality of computational cores configured to receive a plurality of unstructured data elements, each computational core of the plurality of computational cores having properties that are at least partly statistically independent of other of the computational cores, the properties are set independently of each other core.
  10. 10. A non-transitory computer readable medium having stored thereon instructions for causing one or more processing units to execute the method according to claim 1.
  11. 11. A system for identification of a deviation from a periodic behavior pattern in a sequence of multimedia content segments, comprising:
    a processing system; and
    a memory connected to the processing system, the memory containing instructions that, when executed by the processing system, configure the system to:
    generate at least one signature for each multimedia content segment of the sequence of multimedia content segments;
    compare at least two of the generated signatures to detect a periodic behavior pattern, wherein the compared at least two signatures represent consecutive multimedia content segments; and
    compare at least one signature of at least a subsequently received multimedia content segment to at least one signature representing the periodic behavior pattern to identify a deviation from the periodic behavior pattern.
  12. 12. The system of claim 11, wherein each multimedia content segment of the sequence of multimedia content segments is at least one of: an image, a graphic, a video stream, a video clip, an audio stream, an audio clip, a video frame, a photograph, images of signals, and portions thereof.
  13. 13. The system of claim 11, wherein the periodic behavior pattern is related to at least one of: an object that appears in the sequence of multimedia content segments in regular time intervals, an event that occurs through the sequence of multimedia content segments in regular time intervals, and an activity that occurs through a number of consecutive segments in regular time intervals.
  14. 14. The system of claim 13, wherein the object, the event, and the activity are determined using a context of the received sequence of multimedia content segments.
  15. 15. The system of claim 11, wherein the periodic behavior pattern is detected upon identification of matching of signatures of a number of consecutive content segments exceeding a first threshold.
  16. 16. The system of claim 15, wherein at least two signatures match when the at least to signatures overlap more than a first preconfigured threshold level.
  17. 17. The system of claim 11, wherein the system is further configured to:
    generate a baseline signature based on the periodic behavior pattern, wherein identifying the deviation from the periodic behavior pattern further includes comparing the at least one signature of the at least a subsequently received multimedia content segment to the baseline signature.
  18. 18. The system of claim 11, wherein the system is further configured to:
    identify a multimedia content segment representing the periodic behavior pattern; and
    generate a representative segment signature based on the identified multimedia content segment representing the periodic behavior pattern, wherein identifying the deviation from the periodic behavior pattern further includes comparing the at least one signature of the at least a subsequently received multimedia content segment to the representative segment signature.
  19. 19. The system of claim 11, further comprising:
    a signature generator system, wherein each signature is generated by the signature generator system, wherein the signature generator system includes a plurality of computational cores configured to receive a plurality of unstructured data elements, each computational core of the plurality of computational cores having properties that are at least partly statistically independent of other of the computational cores, the properties are set independently of each other core.
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IL18541407A IL185414D0 (en) 2005-10-26 2007-08-21 Large-scale matching system and method for multimedia deep-content-classification
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US12195863 US8326775B2 (en) 2005-10-26 2008-08-21 Signature generation for multimedia deep-content-classification by a large-scale matching system and method thereof
US8415009 true 2009-04-07 2009-04-07
US12434221 US8112376B2 (en) 2005-10-26 2009-05-01 Signature based system and methods for generation of personalized multimedia channels
US13344400 US8959037B2 (en) 2005-10-26 2012-01-05 Signature based system and methods for generation of personalized multimedia channels
US13624397 US9191626B2 (en) 2005-10-26 2012-09-21 System and methods thereof for visual analysis of an image on a web-page and matching an advertisement thereto
US13770603 US20130191323A1 (en) 2005-10-26 2013-02-19 System and method for identifying the context of multimedia content elements displayed in a web-page
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US14509543 US9396435B2 (en) 2005-10-26 2014-10-08 System and method for identification of deviations from periodic behavior patterns in multimedia content
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