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A Combined Hough Transform based Edge Detection and Region Growing Method for Region Extraction  

N.T.B., Nguyen (KAIST 전자전산학부 전산학전공)
Kim, Yong-Kwon (KAIST 전자전산학부 전산학전공)
Chung, Chin-Wan (KAIST 전자전산학부 전산학전공)
Lee, Seok-Lyong (한국외국어대학교 산업경영공학과)
Kim, Deok-Hwan (인하대학교 전자공학과)
Publication Information
Journal of KIISE:Databases / v.36, no.4, 2009 , pp. 263-279 More about this Journal
Abstract
Shape features in a content-based image retrieval (CBIR) system are divided into two classes: contour-based and region-based. Contour-based shape features are simple but they are not as efficient as region-based shape features. Most systems using the region-based shape feature have to extract the region firs t. The prior works on region-based systems still have shortcomings. They are complex to implement, particularly with respect to region extraction, and do not sufficiently use the spatial relationship between regions in the distance model In this paper, a region extraction method that is the combination of an edge-based method and a region growing method is proposed to accurately extract regions inside an object. Edges inside an object are accurately detected based on the Canny edge detector and the Hough transform. And the modified Integrated Region Matching (IRM) scheme which includes the adjacency relationship of regions is also proposed. It is used to compute the distance between images for the similarity search using shape features. The experimental results show the effectiveness of our region extraction method as well as the modified IRM. In comparison with other works, it is shown that the new region extraction method outperforms others.
Keywords
content-based image retrieval; region extraction; IRM; shape features;
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