Summary
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A New Framework for Constructing Accurate Affine Invariant Regions Li TIAN Sei-ichiro KAMATA Publication IEICE TRANSACTIONS on Information and Systems Vol.E90-D No.11 pp.1831-1840 Publication Date: 2007/11/01 Online ISSN: 1745-1361 DOI: 10.1093/ietisy/e90-d.11.1831 Print ISSN: 0916-8532 Type of Manuscript: PAPER Category: Image Recognition, Computer Vision Keyword: affine invariant region, Path Growing, Thresholding Seeded Growing Regions, ellipse fitting, near-duplicate detection, Full Text: PDF(1.8MB)>>
Summary: In this study, we propose a simple, yet general and powerful framework for constructing accurate affine invariant regions. In our framework, a method for extracting reliable seed points is first proposed. Then, regions which are invariant to most common affine transformations can be extracted from seed points by two new methods the Path Growing (PG) or the Thresholding Seeded Growing Region (TSGR). After that, an improved ellipse fitting method based on the Direct Least Square Fitting (DLSF) is used to fit the irregularly-shaped contours from the PG or the TSGR to obtain ellipse regions as the final invariant regions. In the experiments, our framework is first evaluated by the criterions of Mikolajczyk's evaluation framework [1], and then by near-duplicate detection problem [2]. Our framework shows its superiorities to the other detectors for different transformed images under Mikolajczyk's evaluation framework and the one with TSGR also gives satisfying results in the application to near-duplicate detection problem. | ||||||||||
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