Color Image Retrieval based on Primitives of Color Moments J.-L. Shih, L.-H. Chen, IEE Proceeding-Vision, Image and Signal Processing, Vol. 149 No. 6, pp. 370 -376, Dec. 2002. Advisor：Prof. Chang Chin-Chen Student：Chen Yan-Ren Date：2003/03/25

Outlines Introduction Proposed Method Extraction of Primitives of Color Moments Color Image Retrieval Relevance Feedback Algorithm Experimental Results Conclusions

Introduction ...... ...... Image Retrieval Text-based Content-based Keyword Description Color Shape ...... Color Histogram Color Moments ......

Proposed Method Flowchart Extract Features (Primitives) Query Image Similarity Measure Matched Results Image Database Relevance Feedback Algorithm Features Database

Extraction of Primitives of Color Moments Image Divide Image Y, I, Q Color Space Extract Primitives Cluster Color Moments Extract Color Moments

Color Moments Y component P1 P2 … Pj I component Q component M: moment N: total pixels P: color value i: ith component j: jth pixel in i h: total M in i z: h×3 : weights for Y,I,Q CT: feature vector h=1, is mean of i component h=2, is standard deviation of i component (1×30,1×7.07)

Primitive of the Image (1) Y(1×30,1×7) I(2×10,2×2) Q(1.5×20,1.5×4) a M: moment i: ith component h: total M in i z: h×3 : weights for Y,I,Q a: ath block of the image CB: feature vector

Primitive of the Image (2) Clusters CBj CBj CBj PC1 CBj CBj CBj PCk CBj PC2 CBj CBj cba,i cba,i cba,i cba,i cba,i pc1,1 pc1,2 cba,i pc1,z cba,i cba,i cba,i cba,i Y e.g. 　 Block M1 M2 PC1 pc1,1 2 20 4 3 23 5 pc1,2 26 6 1 30 7 Weight=1 Threshold=5 M: moment h: total M in i z: h×3 k: kth cluster n: size of kth cluster J:1,2,...,nk a: ath block of the image CB: feature vector PC: primitive (central vector)

Color Image Retrieval – Similarity Measure Query Image Features Distance calculate Minimum Distance Features in Database Match Results

Relevance Feedback Algorithm Proposed method Color moment Color set Color correlograms Dominant color Color Layout Color structure... User Interface Features Database Relevance Feedback Algorithm Image Database

Retrieval Results from D2 Database

Precision Comparison on D1 Database (1) (a) The precision curves. (b) The precision vs. recall curves (T = 100) N: number of relevant images retrieved T: total number of relevant images K: total number of retrieved images

Precision Comparison on D1 Database (2) K=50, T=100

Precision Comparison on D2 Database

Conclusions Proposed a image retrieval method based on primitives of color moments. The color moments of all blocks are extracted and clustered. Central vectors are considered as primitives (Feature vectors). Similarity measure is used to perform color image retrieval. Relevance feedback algorithm determines the most appropriate feature.