IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 7, NO. 1, FEBRUARY 2005, pp. 105-113

1. FRIP: A Region-Based Image Retrieval Tool Using Automatic Image Segmentation and Stepwise Boolean AND Matching IEEE TRANSACTIONS ON MULTIMEDIA, VOL. 7, NO. 1, FEBRUARY 2005, pp. 105-113 ByoungChul Ko and Hyeran Byun Reporter: Jen-Bang Feng

2. Outline • Image Retrieval • Content-Based Image Retrieval • The Proposed Scheme • Experimental Results

3. Image Retrieval Image retrieval scheme Image DB Features Features Features Features Features Features Features Searching Results Compare Query Image Image retrieval scheme Feature

4. Content-Based Image Retrieval • From text-based retrieval scheme • WWW search engine • Query-by-image in early 90’s • From global to local (region) • Region Of Interest

5. The Proposed Scheme • Image Segmentation • Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem • Iterative Level Using Region Labeling and Iterative Region Merging • Feature extraction • Color • Texture • Normalized Area • Shape and Location • Stepwise Similarity Matching

6. Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem • Adaptive Circular Filter Image (RGB) Image (CIE Lab) Smoothed Image (CIE Lab) Color histogram Remove middle frequency Separate regions by circular filters Regions Regions Regions Regions

7. Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem a is similar to c in color but a is closer to b than c Example of circular filtering process

8. Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem Three circular filters 3x3, 7x7, 11x11 CM: the most frequently observed histogram bins CM: other bins cx,y: center value of CM MC: the major class color

9. Two-Level Segmentation Using Adaptive Circular Filter and Bayes’ Theorem division according to the edge distribution Selected filter, 3x3, 7x7, 11x11 Segmentation result Final segmented image

10. Iterative Level Using Region Labeling and Iterative Region Merging Image (RGB) Image (CIE Lab) Smoothed Image (CIE Lab) Color histogram Remove middle frequency Separate regions by circular filters Regions Regions Regions Regions Regions Regions Merge regions

11. Iterative Level Using Region Labeling and Iterative Region Merging For the N neighbor regions If Then merge the regions If the number of regions is larger than 30 Then increase the threshold and repeat the circular filter

12. Feature extraction • Color • Average AL, Aa, Ab • Variance VL, Va, Vb • Color distance ofQ andT

13. Feature extraction • Texture • Biorthogonal wavelet frame (BWF) • The X-Y directional amplitude Xd, Yd • The distance in texture

14. Feature extraction • Normalized Area NPQ = (Size of the region) / (Size of the image)

15. Feature extraction • Shape and Location • The global geometric shape feature • eccentricity • Estimate the bounding rectangle for each segmented region • For the major axis Rmax and minor axis Rmin

16. Feature extraction • Shape and Location • The local geometric shape feature • MRS (modified radius-based shape signature) • invariant under shape’s scaling, rotation, and translation

17. Feature extraction • Shape and Location • The local geometric shape feature • MRS (modified radius-based shape signature) • Extracts 12 radius distance values

18. Stepwise Similarity Matching

19. Experimental Results query: flower best case

20. Experimental Results query: ship worst case