Image indexing and Retrieval Using Histogram Based Methods, 03/6/5資工研一陳慶鋒

Outline Histogram based methods Histogram based methods Implementation Implementation Experiment result Experiment result Future work Future work References References

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

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

Color histogram (cont.) Distance measure: Distance measure: 令原圖為 I ，欲比對的圖為 I’ 令原圖為 I ，欲比對的圖為 I’ 在比對上使用 L 1 -distance ： 在比對上使用 L 1 -distance ： 比對方式： thresholding 比對方式： thresholding

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

Histogram refinement (cont.) CCVs 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 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 ) for color j, the coherence pair is (  j,  j )

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

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

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

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

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

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

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

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

Implementation(cont.) Similarity measure Similarity measure

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

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

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

Future work Use color images Use color images Study more about tech of CBIR 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 , 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 ,1997 [3]G. Pass, R. Zabih, and J. Miller, “Comparing images using color coherence vectors”, Proc. of ACM Multimedia 96, pp , Boston MA USA, 1996