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Image indexing and Retrieval Using Histogram Based Methods,

Image indexing and Retrieval Using Histogram Based Methods,. 03/6/5 資工研一 陳慶鋒. Outline. Histogram based methods Implementation Experiment result Future work References. General formula in successful IR. A feature vector f(I) for image I I and I’ are not “similar”

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Image indexing and Retrieval Using Histogram Based Methods,

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  1. Image indexing and Retrieval Using Histogram Based Methods, 03/6/5 資工研一 陳慶鋒

  2. Outline • Histogram based methods • Implementation • Experiment result • Future work • References

  3. General formula in successful IR • A feature vector f(I) for image I • I and I’ are not “similar” if and only if |f(I)-f(I’)| is large • f(.) should be fast to compute • f(I) should be small in size

  4. Color histogram • For a nn with m colors image I, the color histogram is where p為屬於I的pixel, I(p)為其顏色 , ,for

  5. Color histogram (cont.) • Distance measure: 令原圖為I，欲比對的圖為I’ 在比對上使用L1-distance ： 比對方式：thresholding

  6. Color histogram (cont.2) • Advantages -trivial to compute -robust against small changes in camera viewpoint • Disadvantages -without any spatial information

  7. Histogram refinement The pixels of a given bucket are subdivided into classes based on local feature. Within a given bucket , only pixels in the same class are compared. The local feature which this paper used: Color Coherence Vectors(CCVs)

  8. Histogram refinement (cont.) • CCVs For the discretized color j, the pixels with color j are coherence if they are adjacent(using eight-neighbor), indicated as j, otherwise are incoherence, indicated as j, and total pixel with color j= j+ j, a threshold  is defined as the condition of coherence or not for color j, the coherence pair is (j, j)

  9. Histogram refinement (cont.2) • CCVs (cont.) Comparing CCV with L1 distance: • Distance measure: 比對方式： thresholding

  10. Histogram refinement (cont.3) • Extension Centering refinement Successive refinement

  11. Color correlograms • A new image feature • Robust against large changes in camera viewpoint

  12. Color correlograms (cont.) • A table indexed by color pairs, where the k-th entry for color pair <i, j> specifies the probability of finding a pixel of color j at a distance k from a pixel of color i in the image. The correlogram is The autocorrelogram is

  13. Color correlograms (cont.2) • Properties: -Contains spatial correlation of colors -Easy to compute -The size of feature is fairly small (O(md))

  14. Implementation • Preprocess Sizes of all images are normalized to 192*128 Colors of all images are quantized to 16 Set  of CCV as 2500 Set d of autocorrelogram as 30

  15. Implementation(cont.) • Indexing color histogram CCV

  16. Implementation(cont.) • Indexing(cont.) color autocorrelogram

  17. Implementation(cont.) • Similarity measure

  18. Experiment result Sample queries and answers with ranks for various methods hist:2 ccv:1 auto:3

  19. Experiment result(cont.) hist:12 ccv:11 auto:4 hist:29 ccv:24 auto:15

  20. Experiment result(cont.) hist:8 ccv:9 auto:18 hist:7 ccv:23 auto:15

  21. Future work • Use color images • Study more about tech of CBIR

  22. References [1]G. Pass and R.Zabih, “histogram refinement for content based image retrieval,” IEEE Workshop on Applications of Computer Vision, pp.96-102, 1996 [2] J. Huang, S. R. Kumar, M. Mitra, W. J. Zhu, and R.Zabih, “Image indexing using color correlograms,” Conf. Computer Vision and Pattern Recognit., pp.762-768,1997 [3]G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vectors”, Proc. of ACM Multimedia 96, pp. 65-73, Boston MA USA, 1996

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