Content-based Image Retrieval

1. Content-based Image Retrieval CE 264 Xiaoguang Feng March 14, 2002 Based on: J. Huang. Color-Spatial Image Indexing and Applications. Ph.D thesis, Cornell Univ., 1998.

2. Contents • Introduction. • Color-histogram vs. Correlogram. • Implementations and Results. • Conclusion.

3. Introduction • Motivation of CBIR • Image features for CBIR • Low Level: • Color • Texture • Edge/Shape • Object Level: • Regions

4. 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.

5. 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.

6. 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? Red ? k P2 P1 Image: I

7. Color auto-correlogram • The auto-correlogram of image I for color Ci , distance k: • Integrate both color information and space information.

8. Color auto-correlogram

9. Implementations • Pixel Distance Measures • Use D8 distance (also called chessboard distance): • Choose distance k=1,3,5,7 • Computation complexity: • Histogram: • Correlogram:

10. 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:

11. Test Environment • 300 Color Images: flowers, people, scene, etc.

12. 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.

13. Test Results • If there is no difference between the query and the target images, both methods have good performance. Correlogram method Query Image (512 colors) 1st 2nd 3rd 4th 5th Histogram method 1st 2nd 3rd 4th 5th

14. Test Results • The correlogram method is more stable to color change than the histogram method. Query Correlogram method: 1st Histogram method: 48th Target

15. Test Results • The correlogram method is more stable to large appearance change than the histogram method. Query Correlogram method: 1st Histogram method: 31th Target

16. Test Results • The correlogram method is more stable to contrast & brightness change than the histogram method. Query 3 Query 1 Query 2 Query 4 C: 178th H: 230th C: 1st H: 1st C: 1st H: 3rd C: 5th H: 18th Target

17. 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.

18. Thanks