Journal of Institute of Control, Robotics and Systems (제어로봇시스템학회논문지)

Institute of Control, Robotics and Systems (제어로봇시스템학회)
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Image Retrieval Using Entropy-Based Image Segmentation

엔트로피에 기반한 영상분할을 이용한 영상검색

  • Jang, Dong-Sik (Dept.of Industry System Information Engineering, Korea University) ;
  • Yoo, Hun-Woo (Dept.of Industry System Information Engineering, Korea University) ;
  • Kang, Ho-Jueng (Dept.of Industry System Information Engineering, Korea University)
  • 장동식 (고려대학교 산업시스템정보공학과) ;
  • 유헌우 (고려대학교 산업시스템정보공학과) ;
  • 강호증 (고려대학교 산업시스템정보공학과)
  • Published : 2002.04.01
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Abstract

A content-based image retrieval method using color, texture, and shape features is proposed in this paper. A region segmentation technique using PIM(Picture Information Measure) entropy is used for similarity indexing. For segmentation, a color image is first transformed to a gray image and it is divided into n$\times$n non-overlapping blocks. Entropy using PIM is obtained from each block. Adequate variance to perform good segmentation of images in the database is obtained heuristically. As variance increases up to some bound, objects within the image can be easily segmented from the background. Therefore, variance is a good indication for adequate image segmentation. For high variance image, the image is segmented into two regions-high and low entropy regions. In high entropy region, hue-saturation-intensity and canny edge histograms are used for image similarity calculation. For image having lower variance is well represented by global texture information. Experiments show that the proposed method displayed similar images at the average of 4th rank for top-10 retrieval case.

Keywords

References

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