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Color Image Retrieval based on Primitives of Color Moments

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, Dec. 2002, pp. 370 -376. Advisor ： Prof. Chang, Chin-Chen Student ： Chen, Yan-Ren Date ： 2003/03/25. Outlines. Introduction

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Color Image Retrieval based on Primitives of Color Moments

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  1. 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, Dec. 2002, pp. 370 -376. Advisor：Prof. Chang,Chin-Chen Student：Chen, Yan-Ren Date：2003/03/25

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

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

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

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

  6. Y component P1 P2 … Pj I component Q component h=1, is mean of i component h=2, is standard deviation of i component CT=[ct1,ct2,..,ct6]= =[Y(1×30,1×7.07), I(2×10,2×2), Q(1.5×20,1.5×4)] Color Moments M: moment N: total pixels P: color value i: ith component j: jth pixel in i h: total M in i :weights for Y,I,Q CT: feature vector M: moment N: total pixels P: color value i: ith component j: jth pixel in i h: total M in i :weights for Y,I,Q CT: feature vector

  7. Primitives of the Image (1) Y(1×30,1×7) I(2×10,2×2) Q(1.5×20,1.5×4) 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

  8. Primitives of the Image (2) Clusters PC1 CB4 CB2 CB3 CB1 PC2 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)

  9. Color Image Retrieval – Similarity Measure Query Image Features Distance calculate Minimum Distance Features in Database Matched Results

  10. Relevance Feedback Algorithm Proposed method Color moments Color set Color correlograms Dominant color Color layout Color structure Color histogram... User Interface Features Database Relevance Feedback Algorithm Image Database 1.System give query results by combined features. 2.User choices r similar images. 3.According user response, R.F.A choices query method.

  11. Retrieval Results from D2 Database

  12. Precision Comparison on D1 Database (1) N=50×0.8=40 N: number of relevant images retrieved K: total number of retrieved images

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

  14. Precision Comparison on D2 Database

  15. 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.

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