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Image Similarity Retrieval using an Scale and Rotation Invariant Region Feature  

Yu, Seung-Hoon (인하대학교 전자공학과)
Kim, Hyun-Soo (인하대학교 전자공학과)
Lee, Seok-Lyong (한국외국어대학교 산업경영공학부)
Lim, Myung-Kwan (인하대학교 의과대학 영상의학과)
Kim, Deok-Hwan (인하대학교 전자공학과)
Publication Information
Journal of KIISE:Databases / v.36, no.6, 2009 , pp. 446-454 More about this Journal
Abstract
Among various region detector and shape feature extraction method, MSER(Maximally Stable Extremal Region) and SIFT and its variant methods are popularly used in computer vision application. However, since SIFT is sensitive to the illumination change and MSER is sensitive to the scale change, it is not easy to apply the image similarity retrieval. In this paper, we present a Scale and Rotation Invariant Region Feature(SRIRF) descriptor using scale pyramid, MSER and affine normalization. The proposed SRIRF method is robust to scale, rotation, illumination change of image since it uses the affine normalization and the scale pyramid. We have tested the SRIRF method on various images. Experimental results demonstrate that the retrieval performance of the SRIRF method is about 20%, 38%, 11%, 24% better than those of traditional SIFT, PCA-SIFT, CE-SIFT and SURF, respectively.
Keywords
image similarity retrieval; MSER; image pyramid; region feature descriptor;
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