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An Image Retrieval System with Automatic Query Modification

Explore how color, shape, and texture information, along with central moments, facilitate image retrieval. Learn about LUV color clustering, segmentation, and feature extraction methods to refine search results. Experiment with intra-query learning and user feedback for improved accuracy.

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An Image Retrieval System with Automatic Query Modification

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  1. An Image Retrieval System with Automatic Query Modification Source: IEEE Transactions on Multimedia 2002 Authors: Gaurav Aggarwal, Ashwin T. V. and Sugata Ghosal Speaker: Chih-Yang Lin

  2. Problem DB Desired image internet

  3. Problem • Overhead in database search • Communication over the WWW based on client-server environments

  4. High-level classification of CBIR systems

  5. QBIC

  6. VisualSEEK

  7. Blobworld

  8. Architecture of iPURE

  9. Color Region Segmentation • LUV (applied in TV system proposed by CIE)

  10. Color clustering Test image RGB space HSV space LUV space

  11. Segmentation by LUV

  12. Color region segmentation Original image Over-segmented

  13. Color region segmentation (cont.) Hopfield network Region merged Shape regularized

  14. General image segmentation Over segmentation

  15. Feature extraction • Average LUV color of the segment (color) • Seize (shape) • Orientation axis (position) • Three central moments (shape) • Invariant to translation, rotation and scale change • Texture information

  16. Central moments • The first central moment is the distribution average • The second central moment is the variance • The third and fourth moments about the mean are used to define the stadardized moments used to define skewness and peakedness

  17. Central moments (example)

  18. Image retrieval

  19. Segment modification • Color modification • Flip colors values • Position modification • Size modification • Orientation modification

  20. Experiments Without intra-query learning Color modification

  21. Experiments (cont.) Choose multi-objects

  22. Experiments (cont.) Without intra-query learning

  23. Experiments (cont.) User feedback Reject rotated horse

  24. Experiments (cont.) After the first iteration of learning

  25. Experiments (cont.) After the second iteration of learning

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