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Green Image
Issue No. 09 - Sept. (2012 vol. 34)
ISSN: 0162-8828
pp: 1827-1841
Yu Sun , Dept. of Electr. Eng., Univ. of California, Riverside, CA, USA
Bir Bhanu , Center for Res. in Intell. Syst., Univ. of California, Riverside, CA, USA
This paper presents a new symmetry-integrated region-based image segmentation method. The method is developed to obtain improved image segmentation by exploiting image symmetry. It is realized by constructing a symmetry token that can be flexibly embedded into segmentation cues. Interesting points are initially extracted from an image by the SIFT operator and they are further refined for detecting the global bilateral symmetry. A symmetry affinity matrix is then computed using the symmetry axis and it is used explicitly as a constraint in a region growing algorithm in order to refine the symmetry of the segmented regions. A multi-objective genetic search finds the segmentation result with the highest performance for both segmentation and symmetry, which is close to the global optimum. The method has been investigated experimentally in challenging natural images and images containing man-made objects. It is shown that the proposed method outperforms current segmentation methods both with and without exploiting symmetry. A thorough experimental analysis indicates that symmetry plays an important role as a segmentation cue, in conjunction with other attributes like color and texture.
matrix algebra, computational geometry, feature extraction, image segmentation, segmentation cue, reflection symmetry, symmetry-integrated region-based image segmentation, image symmetry, SIFT operator, global bilateral symmetry, symmetry affinity matrix, symmetry axis, region growing algorithm, multiobjective genetic search, Image segmentation, Segmentation algorithms, comparison of segmentation algorithms., Local and global symmetry, region growing, symmetry affinity, segmentation and symmetry evaluation

Yu Sun and Bir Bhanu, "Reflection Symmetry-Integrated Image Segmentation," in IEEE Transactions on Pattern Analysis & Machine Intelligence, vol. 34, no. , pp. 1827-1841, 2012.
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