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Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts
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Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts

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Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts. …

Near-Duplicate Video Detection Using Temporal Patterns of Semantic Concepts.

Paper presented at The IEEE International Symposium on Multimedia (ISM2009) in San Diego, California, USA.

http://www.computer.org/portal/web/csdl/doi/10.1109/ISM.2009.93

Published in Technology
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  • Interesting article, thanks. The area of video fingerprinting and duplicate video search seems to became popular nowdays. I saw some other papers related to this and even commercial products. Thus Duplicate Video Search definitelly uses some video fingerprinting to detect duplicates on user's PC. Volicon uses video fingerprinting to monitor TV broadcasts. Zeitera and Video Fingerprinting provide SDKs.
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  • 1. Near-Duplicate Video Detection UsingTemporal Patterns of Semantic Concepts
    IEEE International Symposium on Multimedia
    San Diego, California, USADecember 14-16, 2009
    Hyun-seok Min, Jaeyoung Choi, Wesley De Neve, Yong Man Ro
    Image and Video Systems Lab
    Department of Electrical Engineering
    Korea Advanced Institute of Science and Technology (KAIST)
    Daejeon, Republic of Korea
  • 2. Overview
    Introduction
    Near-duplicates
    Semantic video signatures
    Experimental results
    Conclusions
    2 /20
  • 3. Introduction
    Importance of duplicate video detection
    prevents cluttering of search results
    prevents copyright infringement
    3 /20
    Search results for the query “I will survive Jesus”
    A significant number of search results are near-duplicates!
  • 4. Definition of Near-duplicates
    Identicalor approximately identical videos
    photometric variations
    e.g., change of color and lighting
    editing operations
    e.g., insertion of captions, logos, and borders
    speed changes
    e.g., addition or removal of frames
    semantic concepts
    e.g., ‘road’, ‘sand’, ‘snow’ , ….
    4 /20
  • 5. Examples of Near-duplicates
    original videos
    near-duplicates
    transformation
    (cam cording, insertion of subtitles)
    5 /20
    transformation
    (blur)
  • 6. Video Signatures
    • What is a video signature?
    • 7. represents a video segment with a unique set of features
    • 8. Conventional video signatures
    • 9. often created by extracting low-level visual features fromvideo frames
    6 /20
    video content
    featureextraction
    video signature

  • 10. Use of Low-level Visual Features forCreating a Video Signature
    Problem
    near-duplicates may not be visually similar
    original video
    near-duplicate
    transformation
    (cam cording, insertion of subtitles)
    Visual match? No!
    video signature
    video signature


    7 /20
  • 11. Semantic Similarity
    Observation
    near-duplicates often contain similar semantics
    original video
    near-duplicate
    transformation
    (cam cording, insertion of subtitles)
    Semantic match? Yes!
    Semantic concepts:
    Semantic concepts:
    indoor, man, face, …
    indoor, man, face, …
    8 /20
  • 12. Use of Semantic Concepts forCreating a Video signature
    Semantic concept detection
    traditionally used for classifying video clips into several predefined concepts
    Problem
    limited number of semantic concepts can be detected
    Solution
    use of temporal variation of semantic concepts
    different from video sequence to video sequence
    9 /20
  • 13. Semantic Video Signature Creation (1/2)
    Semantic video signature creation
    A1
    A2
    A3


    Semantic video signature
    V
    video
    shots
    key frames

    concept classification
    classifier for ‘Street’
    classifier for ‘Beach’
    classifier for ‘Tree’
    Ai
    AN
    N: the number of shots
    M: the number of predefined semantics
    Ci: ith predefined semantic concept
    semantic video signature
    si

    sN
    s2
    s1

    10 /20
  • 14. Semantic Video Signature Creation (2/2)
    11 /20
    original video
    near-duplicate
    transformation




    Semantic video signature of original video
    Semantic video signature of near-duplicate
  • 15. Matching Procedure
    12 /20
    Semantic video signature of near-duplicate
    Semantic video signature of original video
  • 16. Experimental Setup (1/3)
    Reference database
    video sequences taken from TRECVID2007
    over 9 hours of video data
    format: MPEG-1
    resolution: 352X288
    frame rate: 25 frame per second (fps)
    Screenshots
    13 /20
  • 17. Experimental Setup (2/3)
    Creation of query video (near-duplicate) set
    number of query video sequences
    64 in total
    average length of the query video sequences
    3 minutes
    Process for generating query video sequences
    original video
    sampling
    subvideoof original video
    transformation
    query video
    14 /20
  • 18. Experimental Setup (3/3)
    Transformations used
    spatial transformations
    Gaussian blur
    logo insertion
    letter-box
    resizing
    temporal transformations
    change of frame rate
    original
    15 /20
  • 19. Experimental Results: Spatial Transformation (1/2)
    16 /20
    The precision increases as the threshold value decreases, while in turn, the recall value decreases.
    blur
    letter-box
  • 20. Experimental Results: Spatial Transformation (2/2)
    17 /20
    Ordinal measurement does not work well with logo insertion, compared to the proposed method.
  • 21. Experimental Results: Temporal Transformation
    18 /20
    The proposed method is robust against temporally modified video sequences.
  • 22. Conclusions
    Proposed the use of semantic video signatures for near-duplicate video detection
    relies on a number of semantic concepts detected along the temporal axis
    Experimental results
    indicate that the use of a semantic video signature looks promising
    Future work
    improving the accuracy of semantic concept detection
    use of additional semantic concepts
    19 /20
  • 23. Any questions or comments?
    20/20