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

2. Outline Color histogram Histogram refinement Color correlogram
Pros. and Cons.
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. Pros and Cons Performance in IR
correlogram > refinement > histogram
Time complexity
histogram > refinement > correlogram
* “>” is “better than”

15. Future work
Implement color correlogram
Study more about tech of CBIR

16. 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