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Content-Based Image Retrieval using the EMD algorithm

Technion - Israel Institute of Technology Department of Electrical Engineering The Vision Research and Image Science Laboratory. Content-Based Image Retrieval using the EMD algorithm. Igal Ioffe George Leifman Supervisor: Doron Shaked Winter-Spring 2000. Project Goal. Source Image.

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Content-Based Image Retrieval using the EMD algorithm

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  1. Technion - Israel Institute of Technology Department of Electrical Engineering The Vision Research and Image Science Laboratory Content-Based Image Retrieval using the EMD algorithm Igal Ioffe George Leifman Supervisor: Doron Shaked Winter-Spring 2000

  2. Project Goal Source Image Color Image DB Similar Images • Estimate similarity between pairs of images • Order the images accounting to similarity to the source image by query

  3. System Overview DB Imagefeatures Query Queryprocess Distance Similarimages Image

  4. Overview:Images & Histograms

  5. Overview: Distance • Minkowski-form distance (L2) • EMD – Earth Movers Distance

  6. Overview: quantization • Summarizing the image content • Reducing high computation complexity Original Image (20154 colors) Quantized Image (15 colors) Quantized Image (5 colors)

  7. Research Issues • Color Quantization algorithms • Quad Tree clustering • Different color spaces • EMD - Earth Movers Distance algorithm

  8. Median Cut vs. Maximum Diversity • Maximum Diversity better than Median Cut for small number of colors (<10) Median Cut Maximum Diversity 3 colors 2 colors

  9. Problems with Histogram

  10. Quad Tree Clustering • Recursive cluster definition • Dynamic stop constraints

  11. Q.Tree Clustering Examples

  12. RGB color space • linear combination of red, green, blue • used to represent image pixels • CIE LAB color space • closer to human vision system Color Spaces

  13. EMD • Bipartite network flow problem • Can be formalized as a well known transportation problem from linear programming field • Minimize- cost Efficient and fast Simplex based solutions

  14. Principal Block Scheme • (a) Color Image Database • (b) Preprocess each image • (c) Store properties of each image in file • (d) Start data base navigation

  15. DB Creator demo

  16. DB Navigator demo

  17. Results

  18. Why Visual C++ ? Graphic user-friendly interface Faster thanMatlab C++ Object Oriented Design Patterns Usage of MFC: effective and convenient way to manipulate large database structures, information reordering and querying (files, strings, array, etc)

  19. Code Optimizations Effective Cache Usage Decreasing data dependencies in out-of-order execution Loop Unrolling Using Multi-Threading to achieve performance gain on Multi-Processor systems

  20. Code Optimizations - Examples struct Rec{ Key key; Rec *next; Data data; }; struct Rec{ Key key; Data data; Rec *next; }; for (i=0; i<N/2; i+=2) { acc1+=a[i]; acc2+=a[i+1]; } acc = acc1 + acc2; for (i=0; i<N; i++) { acc+=a[i]; }

  21. Conclusions EMD captures wellperceptual similarity or dissimilarity of images Using both color histogram and image cluster map improves the results versus histogram alone There is no preferable color space, but their combination leads to better results

  22. Issues For Further Research Including texture properties in image description Testing the application on very large image data bases (> 10000 images) Handling various images transformations, e.g. partial image, scaling, rotation More advanced image feature combination,including color,texture and position

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