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Enhanced Mutimedia Search

Enhanced Mutimedia Search

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    Seminar Seminar Presentation Transcript

    • ENHANCED MULTIMEDIA SEARCH Lakshman Prasad 4MC02EC032 VIII semester E&C
    • Multimedia Search Types: Multimedia Search
      • Content based
        • Indexing, classifying
        • ‘ Widely used today- Google
        • Human’ factor
        • Efficient when accurately indexed
      • Similarity based
        • Correlation of data / feature vectors
        • Infallible
        • Scope of usage very high
        • Efficient for multimedia search
    • Similarity Based Search Search  Similarity based
      • Use of ‘raw image data’ correlation
        • Direct correlation of binary data
        • Efficient for exact matching
      • Use of extracted ‘feature vectors’
        • Extraction of ‘feature vectors’
        • Efficient when approximate matching suffices
    • ‘ Feature Vectors’ Search  Similarity based  Feature vectors
      • Full histogram comparison
        • Direct comparison of histograms
        • Simpler implementation
        • Inefficient for large data.
      • Use of ‘histogram pruning’
        • Comparison of parts of histogram
        • Complex implementation
        • Efficient search for large data
    • ‘ Enhanced Multimedia Search =’ Search  Similarity based  Feature vectors  Histogram Pruning
      • Search
        • Similarity based search
          • Feature vectors extracted similarity based search
            • “ Histogram pruning based feature vector extracted similarity based search”
      • “ Time series Active Search Algorithm”
    • Algorithm
      • Apply windows of query signal length
      • Extract feature vectors from query signal
      • Extract feature vectors from stored signal
      • Classify feature vectors
      • Obtain histograms
      • Determine threshold of comparison
      • Compare histograms
      • Notify when similar / accurate
      • Otherwise, shift forward the window, prun
      • Obtain next Histogram, repeat
    • Time Series Active Search
    • Feature vector used
      • Audio- Short term f spectrum thro’ BPF
        • where k is the sampled time,
        • f j (k) is the normalized short time power spectrum by,
        • where y j (t) is output WF of BPF j at t.
        • M is the time support of feature vector
        • N is the # of frequency channels
      • Video- Color distribution
    • Implementation details
      • Histogram
      • Similarity
      • Pruning
      • Detection criterion
    • Histogram Pruning Analogy
      • EXAMPLE
      • H ERE IS A SIMPLE EXAMPLE
      • E XAMPLE
      • H E RE IS A SIMPLE EXAMPLE
      • E XAMPLE
      • H E R E IS A SIMPLE EXAMPLE
      • E XAMPLE
      • HER E IS A SIMPLE EXAMPLE
      • E XAMPLE
      • HER E IS A SIMPLE EXAMPLE
      • E XAMPLE
      • HERE I S A SIMPLE EXAMPLE
      • … ......
      • E XAMPLE
      • HERE IS A SIMPLE E XAMPLE
      • EX AMPLE
      • HERE IS A SIMPLE E X AMPLE
      • ………
      • EXAMPLE
      • HERE IS A SIMPLE EXAMPLE
    • ‘ AND’ search
    • Experimental results: Speed
      • 48h stored, 15s query
      • PIII 933 MHz PC, Linux
      • Audio: VHS HI-Fi, 11kHz sampling f, 16 bit Q, 7ch II order IIR BPF, M=110
      • Video: NTSC, 29.97 fps, 160x120 pixels/image, W=16
    • Experimental results: Accuracy
      • 60m TV broadcast used as query & stored signal
      • Random choice
      • 100 times repeated
      • Gaussian noise for audio
      • MPEG1 compression for video
    • Experimental results: Summary
      • Matches reduced to 1/291 (video) and , 1/501 (audio), very fast
      • Accurate and reasonable robust with AWGN and MPEG1 compression
    • Extensions to Algorithm
      • ‘ OR’ model of parallel search
      • Feature Distortion Absorption model
      • Dynamic Threshold value determination
    • Applications
      • Statistical analysis of broadcasted music, TV / radio commercials
      • Copyright management on Internet
      • ‘ Truly similar search’- similar music
      • Fastening of biometric security algorithms
    • Conclusion
      • Discussed algorithm enables a faster and more efficient search of audio and video queries from long running audio/video
      • More efficient ‘AND’ method discussed, other extensions overviewed
      • Web search engines based on audio and video queries anticipated
    • References
      • “ A quick search method for Audio and Video signals based on histogram pruning” IEEE transactions on multimedia, Vol.5, #3, Sept 2003
      • Other IEEE transactions on Multimedia
      • “ Digital Image Processing” Rafael C Gonzalez, Richard E woods
      • “ Digital Signal Processing” Proakis and Manolakis
      • http://www-igm.univ-mlv.fr/~lecroq/string/node14.html
      • http://ma.i2r.a-star.edu.sg/Staff/~lingyu/FastVSearch.pdf
      • http://www.cs.utexas.edu/users/moore/best-ideas/string-searching/kpm-example.html
      • http://search.aol.co.uk/av_idx?help=av_adv
      • Several links from
      • http://www.google.com/search?hl=en&q=audio+and+video+search&btnG=Google+Search