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

Outline Color histogram Histogram refinement Color correlogram Pros. and Cons. Future work References

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

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

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

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

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)

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)

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

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

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

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

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

Pros and Cons Performance in IR correlogram > refinement > histogram Time complexity histogram > refinement > correlogram * “>” is “better than”

Future work Implement color correlogram Study more about tech of CBIR

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