EP2768168A1 - Verfahren zur Empfehlung von Themen in einem sozialen Netzwerk - Google Patents

Verfahren zur Empfehlung von Themen in einem sozialen Netzwerk Download PDF

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Publication number
EP2768168A1
EP2768168A1 EP13290032.5A EP13290032A EP2768168A1 EP 2768168 A1 EP2768168 A1 EP 2768168A1 EP 13290032 A EP13290032 A EP 13290032A EP 2768168 A1 EP2768168 A1 EP 2768168A1
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EP
European Patent Office
Prior art keywords
multimedia
fragment
social network
threads
descriptive elements
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Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP13290032.5A
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English (en)
French (fr)
Inventor
Abdelkrim Hebbar
Yann Gaste
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Alcatel Lucent SAS
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Alcatel Lucent SAS
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Publication date
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Priority to EP13290032.5A priority Critical patent/EP2768168A1/de
Publication of EP2768168A1 publication Critical patent/EP2768168A1/de
Withdrawn legal-status Critical Current

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    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/35—Arrangements 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
    • H04H60/37—Arrangements 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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04H—BROADCAST COMMUNICATION
    • H04H60/00—Arrangements for broadcast applications with a direct linking to broadcast information or broadcast space-time; Broadcast-related systems
    • H04H60/56—Arrangements characterised by components specially adapted for monitoring, identification or recognition covered by groups H04H60/29-H04H60/54
    • H04H60/58—Arrangements characterised by components specially adapted for monitoring, identification or recognition covered by groups H04H60/29-H04H60/54 of audio

Definitions

  • Such methods mostly rely on historical data, or ratings of other social network users, and do not allow real time following of the social network threads in the social network that are related to a currently attended network stream.
  • the invention has for object a method to recommend social network threads to a user attending a multimedia flow, comprising the steps :
  • the step in which the multimedia analysis unit analyses the content of the multimedia fragment to fill a fragment descriptor containing descriptive elements comprises a step wherein a face recognition subunit performs a face recognition on a picture of the multimedia fragment to extract names of recognized people as descriptive elements.
  • the step in which the multimedia analysis unit analyses the content of the multimedia fragment to fill a fragment descriptor containing descriptive elements comprises a step wherein a sound recognition subunits performs a sound recognition on a sound extract of the multimedia fragment to extract recognized sound source names as descriptive elements.
  • the step in which the multimedia analysis unit analyses content of the multimedia fragment to fill a fragment descriptor containing descriptive elements comprises a step wherein a chromatic analysis subunit performs a chromatic analysis on a picture of the multimedia fragment to extract colour patterns as descriptive elements.
  • Said steps of analysing the social network threads on the social network platform and of extracting descriptive elements from the analysed social network threads may further comprise the steps :
  • FIG 1 is schematically represented a home network 100 with a multimedia receiver 1, comprising in this example a television 3, acting as multimedia display unit, and a base-station 5 connected to the internet and receiving a multimedia stream, here an IPTV (Internet Protocol TeleVision) stream.
  • a multimedia stream here an IPTV (Internet Protocol TeleVision) stream.
  • the multimedia stream may be any other kind of video stream, an audio stream, a slideshow, a text stream such as a news title stream, and the like.
  • the recommendation unit 17 recommends specific network threads searched using search queries in a social network thread database by proposing said threads to the user for reading, the threads being selected according to specific rules chosen for example as a function of the interests of the user, used in the search queries.
  • the fragment extraction unit 13 may use the statistics of the changes in framing and shots as indicators for continuity or for changing the fragment.
  • a speech of a candidate will often correspond to long single shots either of the candidate himself or of the attending public, while a heated discussion between the candidates will correspond to fast alternating shots of said candidates and other intervening people, with similar alternation patterns in the speaking voices.
  • the fragment extraction unit 13 may be configured to store temporarily on memory, in particular cache memory, the multimedia content or packets. The stored data is then processed, so that fragment delimitation markers may be established. The delimited fragments are then sent to the other units or subunits for further processing.
  • the fragment extraction unit 13 may use a change in content nature, for example a transition from speech to music, a recurring jingle, or a change in the speaking voices as fragment delimitation.
  • a systematic fractioning can be done using predetermined time intervals (e.g. 30 seconds or one minute) to allow real time or almost real time recommendation, and to avoid sending and processing multimedia fragments that are too heavy in data amount. Also, to avoid the fragments to be too numerous while having not enough content for a meaningful search to be conducted, a minimal length (e.g. 3 seconds) may be predetermined for the fragments.
  • the fragment extraction unit 13 may also hash the fragments, for example by keeping only every second or third image of a video stream to make the fragments lighter and easier to process. In the case of an audio or radio flow any suitable sound file compression procedure may be used.
  • the fragment extraction unit 13 may comprise an application downloaded and run on the social network browsing node 7, and/or a program stored and run on the base station 5 or on the social network platform.
  • the extracted multimedia fragments are sent to a multimedia analysis unit 15, which converts the multimedia fragments in data usable in search queries, by extracting descriptive elements and properties from the fragments. For example, if the fragments comprise a picture of an actor, the analysis unit may use a face recognition subunit to identify said actor, and isolate his name as a relevant descriptive element to use in search queries.
  • the multimedia analysis unit 15 comprises the associated means to process multimedia data, which may include subunits associated to the aforementioned functions. Said subunits may be shared with the fragment extraction unit 13 for the extracting of the multimedia fragments.
  • the multimedia analysis unit 15 will run voice detection methods to identify the presence of a speech.
  • the analysis unit 15 uses a speech-to-text conversion subunit to transcript the sound in searchable words. If no speech is detected, or in addition or in parallel to the speech-to-text transcription, the analysis unit 15 may use sound or music recognition subunits, for example to identify the title and artist of a played music track, or the name of a sound source (instrument, animal, etc.) and use them as searchable words.
  • the multimedia analysis unit 15 will use for example face or object recognition subunits, to identify people or objects on the multimedia fragment. The names obtained are then used as searchable terms.
  • the multimedia analysis unit 15 may also use structural or descriptive metadata. For example, on an IPTV program, the title, a short synopsis and/or the cast and authors and producers may be forwarded to the user in a transparent fashion, by multiplexing the data with the multimedia content. Said data may for example serve when the user requests information on the program he is currently watching using an integrated information display function of his television.
  • Such metadata about multimedia content is most of the time accessible via application programming interfaces (API), often open and/or public application programming interfaces.
  • API application programming interfaces
  • Such metadata is added in particular by the multimedia flow provider as an additional service.
  • the isolated descriptive elements are used to fill a multimedia fragment descriptor, which contains keywords and statistics extracted from the multimedia fragment, for example a set of weights associated to the keywords and attributed according to the number of occurrences of the considered keyword, or the chronological evolution of specific properties and characteristics of the multimedia fragment.
  • the metrics used to search for related threads 11a, 11b, 11c using the descriptor may comprise the known metrics such as objective image quality index (OQI), structural similarity (SSIM) or the Czenakowski distance (CZD).
  • OQI objective image quality index
  • SSIM structural similarity
  • CZD Czenakowski distance
  • threads 11a, 11b, 11c with elements within a predetermined distance of the descriptive elements of the fragment descriptor according to the chosen metrics are considered relating to the content of the multimedia fragment.
  • the fragment descriptor is then sent to a social thread recommendation unit 17 which is configured to use the fragment descriptor in a search query for social network threads 11a, 11b, 11c which are consequently bound to the content of the currently attended multimedia stream.
  • the thread recommendation unit 17 is therefore connected to the social network client 9, configured to give recommended thread identifiers in answer to a query containing recommendation rules.
  • Identifiers of the returned recommended social threads 11a, 11b, 11c are then forwarded to the social browsing node 7.
  • the social network browsing node 7 then only needs to search for the threads 11a, 11b, or 11c associated to the stored identifiers and display at least part of their content for the user to pick the ones he is interested in.
  • the recommendation unit 17 is connected to a recommendation database 21, which is configured to store the multimedia fragment descriptors and at least part of the returned results.
  • the recommendation unit 17 may search within the recommendation database 21 the previously established fragment descriptor closest to the current one yielding no or insufficient results, and use the returned results corresponding to said closest descriptor.
  • a social network crawler 23 Connected to the social network database 21 is a social network crawler 23, for systematic and thorough browsing of the social thread contents, in particular to forward and update the social network threads that are followed by users.
  • the social network crawler 23 is in particular used to conduct intensive analysis and ordering of the network threads 11a, 11b, 11c to establish thread properties.
  • Figure 2 represents in a schematic fashion the main steps of a particular embodiment of a method 200 to analyse the content of social threads 11a, 11b and 11c stored on the social network database 11 using the social network crawler 23 in order to make the search for specific threads less time and resource consuming, and thus reach minimal delay between media fragment display and associated recommendation.
  • the first step 201 for a gross filtering of topic candidates is the listing of their titles and topics by the network crawler 23. This implies that the threads 11a, 11b, 11c have previously been given one or more topic descriptors, related to their content and containing for example titles and/or tagged keywords.
  • the social network crawler 23 may in particular be configured to store possible synonym words, general associated themes and lexical fields relating to the titles and/or keywords.
  • the third step 205 is the emerging of the data structures in the selected social threads 11a, 11b, 11c.
  • the emerging of the data structures is the conversion from the initial, chronological linear graph of the messages ("timeline") to a conversation tree structure, where the semantic relationship of the messages is taken in consideration.
  • the social crawler 23 uses a semantic analysis subunit and/or metadata of the messages, which may be associated a tag stating that the considered message is a reply, and identify the message of which it is a reply.
  • the third step 205 is the establishing of statistical and topological thread structure properties from the emerged structure.
  • the search for network threads 11a, 11b, 11c that correspond to a multimedia fragment may accordingly be performed by comparing the social topic descriptors and the multimedia fragment descriptors. Consequently, the thorough and resource consuming search in the social threads 11a, 11b, 11c needs only be done periodically, for example once a predetermined amount of modifications (updates) is detected on the network threads 11a, 11b, 11c or at predetermined time intervals.
  • Figure 3 is a schematic flow chart with the main steps of the method 300 to recommend social threads 11a, 11b, 11c represented.
  • the first step 301 is the acknowledgement that a multimedia stream is attended. This corresponds to the launching of the application or program corresponding to the method. If no clear and non empty fragment can be identified, an error message may be sent, prompting the user to check for problems in the multimedia flow reception.
  • step 309 the fragment descriptor is searched for new content in comparison to the previously established descriptors. If no new content is identified, a new fragment is extracted in a return to step 303.
  • network thread descriptors are collected from a social thread descriptor database, for example using a main keyword or semantic element to collect only the descriptors of a set of potentially related social network threads 11a, 11b, 11c in a previous, rapid sorting.
  • the social network thread descriptors established or updated during the social network crawling are all stored in a database DB following the recommendation, or failure to identify a recommended social network thread.

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  • Engineering & Computer Science (AREA)
  • Signal Processing (AREA)
  • Physics & Mathematics (AREA)
  • Acoustics & Sound (AREA)
  • Multimedia (AREA)
  • Information Retrieval, Db Structures And Fs Structures Therefor (AREA)
EP13290032.5A 2013-02-18 2013-02-18 Verfahren zur Empfehlung von Themen in einem sozialen Netzwerk Withdrawn EP2768168A1 (de)

Priority Applications (1)

Application Number Priority Date Filing Date Title
EP13290032.5A EP2768168A1 (de) 2013-02-18 2013-02-18 Verfahren zur Empfehlung von Themen in einem sozialen Netzwerk

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
EP13290032.5A EP2768168A1 (de) 2013-02-18 2013-02-18 Verfahren zur Empfehlung von Themen in einem sozialen Netzwerk

Publications (1)

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EP2768168A1 true EP2768168A1 (de) 2014-08-20

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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040170392A1 (en) * 2003-02-19 2004-09-02 Lie Lu Automatic detection and segmentation of music videos in an audio/video stream
US20100325218A1 (en) * 2009-06-22 2010-12-23 Nokia Corporation Method and apparatus for determining social networking relationships
US20110282906A1 (en) * 2010-05-14 2011-11-17 Rovi Technologies Corporation Systems and methods for performing a search based on a media content snapshot image
US20120054795A1 (en) * 2010-08-31 2012-03-01 Samsung Electronics Co., Ltd. Method and apparatus for providing preferred broadcast information
US20120317046A1 (en) * 2011-06-10 2012-12-13 Myslinski Lucas J Candidate fact checking method and system
US20120331496A1 (en) * 2011-06-22 2012-12-27 Steven Copertino Methods and apparatus for presenting social network content in conjunction with video content

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20040170392A1 (en) * 2003-02-19 2004-09-02 Lie Lu Automatic detection and segmentation of music videos in an audio/video stream
US20100325218A1 (en) * 2009-06-22 2010-12-23 Nokia Corporation Method and apparatus for determining social networking relationships
US20110282906A1 (en) * 2010-05-14 2011-11-17 Rovi Technologies Corporation Systems and methods for performing a search based on a media content snapshot image
US20120054795A1 (en) * 2010-08-31 2012-03-01 Samsung Electronics Co., Ltd. Method and apparatus for providing preferred broadcast information
US20120317046A1 (en) * 2011-06-10 2012-12-13 Myslinski Lucas J Candidate fact checking method and system
US20120331496A1 (en) * 2011-06-22 2012-12-27 Steven Copertino Methods and apparatus for presenting social network content in conjunction with video content

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