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

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

4. 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. 5 The Proposed Scheme 1. Image Segmentation Two-Level Segmentation Using Adaptive Circular Filter and Bayes ’ Theorem Iterative Level Using Region Labeling and Iterative Region Merging 2. Feature extraction Color Texture Normalized Area Shape and Location 3. Stepwise Similarity Matching

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

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

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

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

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

12. 12 Feature extraction Color Average AL, Aa, Ab Variance VL, Va, Vb Color distance of Q and T

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

14. 14 Feature extraction Normalized Area NP Q = (Size of the region) / (Size of the image)

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

16. 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. 17 Feature extraction Shape and Location The local geometric shape feature MRS (modified radius-based shape signature) Extracts 12 radius distance values

18. 18 Stepwise Similarity Matching

19. 19 Experimental Results query: flower best case

20. 20 Experimental Results query: ship worst case

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