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SIFT based Image Similarity Search using an Edge Image Pyramid and an Interesting Region Detection  

Yu, Seung-Hoon (인하대학교 전자공학과)
Kim, Deok-Hwan (인하대학교 전자공학과)
Lee, Seok-Lyong (한국외국어대학교 산업경영공학부)
Chung, Chin-Wan (한국과학기술원 전산학과)
Kim, Sang-Hee (국방과학연구소 지형영상처리팀)
Publication Information
Journal of KIISE:Databases / v.35, no.4, 2008 , pp. 345-355 More about this Journal
Abstract
SIFT is popularly used in computer vision application such as object recognition, motion tracking, and 3D reconstruction among various shape descriptors. However, it is not easy to apply SIFT into the image similarity search as it is since it uses many high dimensional keypoint vectors. In this paper, we present a SIFT based image similarity search method using an edge image pyramid and an interesting region detection. The proposed method extracts keypoints, which is invariant to contrast, scale, and rotation of image, by using the edge image pyramid and removes many unnecessary keypoints from the image by using the hough transform. The proposed hough transform can detect objects of ellipse type so that it can be used to find interesting regions. Experimental results demonstrate that the retrieval performance of the proposed method is about 20% better than that of traditional SIFT in average recall.
Keywords
Image Similarity Search; SIFT; Canny Edge Algorithm; Interesting Region Detection;
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