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Combining Spanning Trees and Normalized Cuts for Internet Retrieval

Combining Spanning Trees and Normalized Cuts for Internet Retrieval. Sharat Chandran 1 and Abhishek Ranjan 2. ViGIL IIT Bombay www.cse.iitb.ac.in/graphics/. 1. 2. Need for CBIR. 1994 : Yahoo Text based search 2002 : Google image search search for “ Apple ” A related problem : CBVR.

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Combining Spanning Trees and Normalized Cuts for Internet Retrieval

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  1. Combining Spanning Trees and Normalized Cuts for Internet Retrieval Sharat Chandran1 and Abhishek Ranjan2 ViGIL IIT Bombay www.cse.iitb.ac.in/graphics/ 1. 2.

  2. Need for CBIR • 1994 : Yahoo Text based search • 2002 : Google image search search for “Apple” • A related problem : CBVR

  3. Previous efforts • Image feature vector indexing: • QBIC(1995), Photobook(1996), WBIIS(1997) etc. • Information loss : shape, location etc. • Image segmentation: • WindSurf(1999), Blobworld(1999), SIMPLIcity(2001) etc. • No iterative refinement

  4. An interactive CBIR system • User enters a query image • System • Quickly segments the query image • Searches images with similar segments • Returns the approximate results • User iteratively refines the results A progressive refinement strategy • Our focus : Quick segmentation

  5. Underlying requirements • Hierarchical image segmentation • Control over levels of segmentation • Fast segmentation for quick response • Intuitive segmentation

  6. Normalized cut (Ncut, Shi et al. ‘00) Global Optimization Good criteria Promising in videos Cost Time : O(n1.5) Space : O(n2) 106 pixels or 200x100, 50 frames video: time : 1,000,000,000units, space : 1,000,000,000,000units Hierarchical segmentation Image with 2 segments

  7. We need … • Speed + Quality How ? Reduce input size fed to algorithm ! Input size : n1/2, Cost : O((n1/2)1.5)

  8. Two step pipeline Hierarchical segmentation Fast grouping O(n log n) n=9x104 Ncut O(n1.5)

  9. Fast grouping • Group similar pixels • Grouping using local variation (PAlgo Pedro et al. 1998) • Uses local properties • Fast : O(n log n) • Produces too many regions for CBIR

  10. Pipeline • i/p PAlgo intermediate Ncut o/p • Pipelining not easy • Unpredictable output size of PAlgo • Needed output size : n1/2 • Keep the quality intact • Solution • Cluster merging

  11. Merging process • Sort the ‘similarity’ • Merge ‘similar’ groups iteratively

  12. Result Pipeline O(n log n) Input N-cut O(n1.5)

  13. Conclusion • A new pipelining strategy • Efficiently combined two approaches • Application in CBIR • Possibilities • Video segmentation • Video retrieval system (CBVR)

  14. Thanks!! Questions ? aranjan@dgp.toronto.edu http://www.dgp.toronto.edu/~aranjan

  15. Why N-cut ? • Edge weights are proportional to similarity

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