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Chapter 12 Multimedia IR: Indexing and Searching

Chapter 12 Multimedia IR: Indexing and Searching. Date: 11/17/2005. Introduction. Feature extraction, feature indexing, distance, similarity query Distance

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Chapter 12 Multimedia IR: Indexing and Searching

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  1. Chapter 12 Multimedia IR: Indexing and Searching Date: 11/17/2005

  2. Introduction • Feature extraction, feature indexing, distance, similarity query • Distance • Editing distance: smallest number of insertions, deletions, and substitutions that are needed to transform the first string to the second • Euclidean distance: • Similarity query • Whole match: the query and the objects are of the same type • Sub-pattern match: 16×16 sub-pattern on 512×512 grey-scale images • Nearest neighbors • All pairs

  3. Correctness of query results • False dismissal: unacceptable • False alarm: can be discarded via post-processing • Spatial access method: R-tree

  4. A generic indexing approach • The whole match problem • Given O1, O2, …, On, D(Oi, Oj), Q,   {Oi | D(Q, Oi)  } • Basic idea of the approach • A quick-and-dirty test • Discard non-qualifying objects • Allow false alarms • Use of SAM

  5. An example (yearly stock-price movements) • Average as the quick-and-dirty test • Large difference  can’t be similar • Small difference  similar  false alarm • f features  reduce false alarms  each object can be mapped into a point in f-dimensional space • No need to test all f-d points ?

  6. Preservation of distance mapping • Exact preservation • No false alarm, no false dismissal • Difficult to find such features • Dimensionality curse • No false dismissal if Dfeature(F(O1), F(O2))  D(O1, O2) • With potential false alarms • The lower bounding lemma

  7. Feature selection • Preserve distance • Carry much information about the corresponding objects to reduce false alarms • Nearest neighbor query • Find the point F(P) that is the nearest neighbor to the query point F(Q) • Issue a range query with Q and  = D(Q, P)

  8. One-dimensional time series • The first day’s value is a bad feature • 365 values  dimensionality curse • Average is better • Discrete Fourier Transform • For a signal x = [xi], i = 0, 1, …, n-1, let XF denote the DFT coefficient at the F-th frequency, F = 0, 1, …, n-1 • Keeping the first f coefficients of the DFT as the features 

  9. The fewer the coefficients that contain most of the energy, the fewer the false alarms, and the faster the response time • The dimensionality curse is avoided with the low-bounding lemma and the energy-concentrating property of the DFT • f = 1 ~ 3

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