0 Two-dimensional color images 2-D color image (QBIC) –Compute a k-element color histogram for each image 16×10 6 → 256 A: color-to-color similarity matrix.

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Presentation transcript:

0 Two-dimensional color images 2-D color image (QBIC) –Compute a k-element color histogram for each image 16×10 6 → 256 A: color-to-color similarity matrix When A is identity matrix  d hist reduces to Euclidean distance Example of A

1 –Two obstacles for applying the F-index method Dimensionality curse O(k 2 ): cross-talk problem –Consider RGB color space Average color of an image x = (R avg, G avg, B avg ) t -

2 – for quick-and-dirty test –Solve the cross-talk problem Allow indexing with SAM Solve the dimensionality curse problem Save CPU time Theorem (Quadratic Distance Bounding) – (), where 1 is constant, depending on A

3

4 Sub-pattern matching –The problem: given sequences S 1, S 2, …, S n, query Q of length Len(Q),   {(S i, k) | D(Q, S i [k:k+Len(Q)-1])   }

5 ST-index –Sliding window w; a data sequence of length Len(S) is mapped to a trail of Len(S)-w+1 points in the feature space

6

7

8 –Divide the trail into sub-trails, each represented by an MBR

9 A dividing method in [FRM 94]

10 –Query processing Queries of length w

11 Queries of length > w

12 –Break the query into p disjoint pieces of length w –Search for each piece, with tolerance –‘OR’ the results and discard false alarms – Can the method be extended to deal with 2-d images? Other applications? –Distance function –Lower-bounding lemma