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Learning-based automatic commercial content detection

  • US 7,164,798 B2
  • Filed: 02/18/2003
  • Issued: 01/16/2007
  • Est. Priority Date: 02/18/2003
  • Status: Expired due to Fees
First Claim
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1. A method for learning-based automatic commercial content detection, the method comprising:

  • dividing program data into multiple segments;

    analyzing the segments to determine visual, audio, and context-based feature sets that differentiate commercial content from non-commercial content; and

    wherein the context-based features are a function of one or more single-side left and/or right neighborhoods of segments of the multiple segments; and

    wherein the single-side left and/or right neighborhoods are (2n+1) neighborhoods of a current segment Ci, and wherein the method further comprises;

    calculating each of the (2n+1) neighborhoods as follows;

    N k = [ N s k , N e k ] = { [ min

    ( e j + α





    k
    , 0
    )
    , e i
    ]
    [ s i , e i ] [ s i , min

    ( s i + α





    k
    , L
    )
    ]


    k <

    0
    k = 0 k >

    0
    ,
    wherein Nk represents 2n+1, n representing a number of neighborhoods left and/or right of Ci, [si, ei] denoting start and end frame numbers for Ci and start and end times for Ci, Nks represents a start frame number for Nk, Nke represents an end frame number for Nk, L indicating a length of the program data, kε

    Z,|k|≦

    n, and α

    a comprising a time step.

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