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Sharath copy

  1. 1. TECHNICAL SEMINAR ON FAST VISUAL RETRIEVALUSING ACCELERATED SEQUENCE MATCHING PRESENTED BY UNDER THE GUIDANCE xxxxxxxxx Mr/Mrs.xxxxxxx (1xx08is041) HOD,DEPT. OF XXX XXXXXX
  2. 2. ABSTRACT We present an approach to represent, match, and index various types of visual data. Primary goal is to enable effective and computationally efficient searches. an image/video is represented by an ordered list of feature descriptors. Similarities b/w such representations are measured by the approximate sequence matching technique. This unifies visual appearance and the ordering information in a holistic manner.
  3. 3. Introduction With the rapid growth in image/video production and distribution industry. Necessary to develop technique which enable users to easily access and organize large volumes of visual data. There are 2 major problems regarded to visual retrieval and its organization i.e., (1) lack of design technique for good visualrepresentation and (2) lack of quantitative metrics which can efficientlymeasure similarities b/w each pair of visual data.
  4. 4. Continued… The above problems concerned with the visual retrieval and its organization can be overcomed using following approaches… (1) approximate sequence matching technique(also calledLevenshtein distance) (2)extension to local alignment(smith-watermanalgorithm) (3) indexing with a Vocabulary tree (4)Fast matching algorithm
  5. 5. 1.Approximate sequence matchingtechnique  compare two pieces of visual data that represented by ordered features.  Its is formulated by Levenshtein distance ci-cost of an operation  The Levenshtein distance is defined as the “minimal cost of sequence of operations that transforms X into Y”
  6. 6. Continued…• Operations restricted in levenshtein distance are as follows (1)insertion δ (ε, a) (2)deletion δ (a, ε) (3)substitution δ (a, b)• Levenshtein distance can be computed using dynamic programming
  7. 7. Simple example for approximatestring matching
  8. 8. 2.Extension to local alignment Extended to search for local alignments b/w two feature sequences. Derives a score v(xi, yj) between two feature vectors xi and yj. v(xi, yj) is +ve if xi and yj are similar. v(xi, yj) is -ve if xi and yj are not similar. Value v(xi, yj) is considered as substitution score. For insertion operation we assign negative scores denoted as v(xi, ε).
  9. 9. Continued… Similarly for deletion operation we assign scores denoted as v(ε, yi). optimal local alignment can be computed using S(i,j)=max{0,S(i-1,j)+v(x, ε),S(i,j-1)+v(ε,yj),S(i-1,j-1)+v(x ,yj)} is called Smith-waterman algorithm compares each descriptor of query to every descriptors in the database.
  10. 10. 3.Indexing with a vocabulary tree Vocabulary tree-used for indexing all feature vectors extracted from database. Indexing each of the visual descriptors can be done hierarchically using the concept of k-means hierarchical clustering. Other advanced vocabulary tree used for indexing problems is adaptive vocabulary tree Adaptive vocabulary tree adapts to the addition/deletion of instances from the database. Advantage-tree need not to be rebuilt in case of adaptive vocabulary tree when database undergo slight changes. It enables the retrieval time to grow sub linearly with respect to the no of frames in the database.
  11. 11. Continued… Levels of an adaptive vocabulary tree grow sub linearly in terms of frame numbers in the database
  12. 12. 4.Fast matching algorithm Tries to filter unnecessary alignments which will not lead to successful matching. Uses visual method called a dot plot. Dot plot puts a dot at (i , j) if descriptor i and descriptor j are similar.
  13. 13. Continued… Diagonal link(i,j) is established if dot j is positioned at the bottom-right corner of dot i. Diagonal links represent contiguous matched descriptor pairs. Gap link(i,j) is established if dot j is bottom-right positioned with respect to dot i. Gap link allows the operation of insertions and deletions.
  14. 14. CONCLUSION Proposed techniques used for effective and computational efficient visual searches. Representation based on sequence of features. We apply the approximate sequence matching method to measure the similarity based on such a representation. Presented the framework using which it speedups the matching process. Fast retrieval of visuals are ensured. Good representation of visual data. Proposed techniques have been demonstrated for use in several visual retrieval applications.
  15. 15. REFERENCES D. A. Adjeroh, M. -C. Lee, and I. King. A distance measure for video sequence similarity matching. M. Bertini, A. D. Bimbo, and W. Nunziati. Video clip matching using MPEG-7 descriptors and edit distance. D. Nister and H. Stewenius. Scalable recognition with a vocabulary tree. J. Law-To, O. Buisson, V. Gouet-Brunet, and N. Boujemaa. Robust voting algorithm based on labels of behavior for video copy detection.
  16. 16. Queries?

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