Content-based Image RetrievalContent-based Image Retrieval
CE 264 Xiaoguang FengMarch 14, 2002
Based on: J. Huang. Color-Spatial Image Indexing and Applications. Ph.D thesis, Cornell Univ., 1998.
Contents
Introduction.Color-histogram vs. Correlogram.Implementations and Results.Conclusion.
Introduction
Motivation of CBIRImage features for CBIR Low Level:
Color Texture Edge/Shape
Object Level: Regions
Color Histogram
The histogram of image I is defined as:For a color Ci , Hci(I) represents the number of pixels of color Ci in image I .
OR:For any pixel in image I, Hci(I) represents the possibility of that pixel is in color Ci.
Most commercial CBIR systems include color histogram as one of the features (e.g., QBIC of IBM).No space information.
Improvement of color histogram
There are several techniques proposed to integrate spatial information with color histograms:
W.Hsu, et al., An integrated color-spatial approach to content-based image retrieval. 3rd ACM Multimedia Conf. Nov 1995.
Smith and Chang, Tools and techniques for color image retrieval, SPIE Proc. 2670, 1996.
Stricker and Dimai, Color indexing with weak spatial constraints, SPIE Proc. 2670, 1996.
Gong, et al., Image indexing and retrieval based on human perceptual color clustering, Proc. 17th IEEE Conf. On Computer Vision and Pattern Recognition, 1998.
Pass and Zabih, Histogram refinement for content-based image retrieval. IEEE Workshop on Applications of Computer Vision, 1996.
Park, et al., Models and algorithms for efficient color image indexing. Proc. Of IEEE Workshop on Content-Based Access of Image and Video Libraries, 1997.
Color auto-correlogram
Pick any pixel p1 of color Ci in the image I, at distance k away from p1 pick another pixel p2, what is the probability that p2 is also of color Ci?
P1
P2
k
Red ?
Image: I
Color auto-correlogram
The auto-correlogram of image I for color Ci , distance k:
Integrate both color information and space information.
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Color auto-correlogram
Implementations
Pixel Distance Measures Use D8 distance (also called chessboard distance):
Choose distance k=1,3,5,7 Computation complexity:
Histogram: Correlogram:
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)*134( 2n
)( 2n
Implementations
Features Distance Measures: D( f(I1) - f(I2) ) is small I1 and I2 are similar. Example: f(a)=1000, f(a’)=1050; f(b)=100,
f(b’)=150 For histogram:
For correlogram:
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Test Environment
300 Color Images: flowers, people, scene, etc.
Test Environment
Image quantized to 512 and 64 colors.First calculate the correlogram and histogram of the 300 images, saved as data file.For each query, calculate the correlogram and histogram of the query image; compare it with the data file; sort the feature distances.The order of the target image in the sorted searching result measures the performance.
Test Results
If there is no differenceno difference between the query and the target images, both methods have good performance.
Query Query ImageImage
(512 colors)(512 colors)
CorrelograCorrelogram methodm method
Histogram Histogram methodmethod
1s1stt
2nd2nd 3r3rdd
4t4thh
5t5thh
1s1stt
2nd2nd 3r3rdd
4t4thh
5t5thh
Test Results
The correlogram method is more stable to color changecolor change than the histogram method.
QuerQueryy
TargetTarget
Correlogram method: 1st
Histogram method: 48th
Test Results
The correlogram method is more stable to large appearance changelarge appearance change than the histogram method.
QuerQueryy
TargetTarget
Correlogram method: 1st
Histogram method: 31th
Test Results
The correlogram method is more stable to contrast & brightness changecontrast & brightness change than the histogram method.
Query Query 11
TargetTarget C: 178th
H: 230th
Query Query 22
Query Query 33
Query Query 44
C: 1st
H: 1st
C: 1st
H: 3rd
C: 5th
H: 18th
Conclusion
The color correlogram describes the global distribution of local spatial correlations of colors.It’s easy to compute.It’s more stable than the color histogram method.
ThanksThanks