DocumentCode :
3606884
Title :
Common Visual Pattern Discovery via Nonlinear Mean Shift Clustering
Author :
Linbo Wang ; Dong Tang ; Yanwen Guo ; Do, Minh N.
Author_Institution :
Key Lab. of Intell. Comput. & Signal Process., Anhui Univ., Hefei, China
Volume :
24
Issue :
12
fYear :
2015
Firstpage :
5442
Lastpage :
5454
Abstract :
Discovering common visual patterns (CVPs) from two images is a challenging task due to the geometric and photometric deformations as well as noises and clutters. The problem is generally boiled down to recovering correspondences of local invariant features, and the conventionally addressed by graph-based quadratic optimization approaches, which often suffer from high computational cost. In this paper, we propose an efficient approach by viewing the problem from a novel perspective. In particular, we consider each CVP as a common object in two images with a group of coherently deformed local regions. A geometric space with matrix Lie group structure is constructed by stacking up transformations estimated from initially appearance-matched local interest region pairs. This is followed by a mean shift clustering stage to group together those close transformations in the space. Joining regions associated with transformations of the same group together within each input image forms two large regions sharing similar geometric configuration, which naturally leads to a CVP. To account for the non-Euclidean nature of the matrix Lie group, mean shift vectors are derived in the corresponding Lie algebra vector space with a newly provided effective distance measure. Extensive experiments on single and multiple common object discovery tasks as well as near-duplicate image retrieval verify the robustness and efficiency of the proposed approach.
Keywords :
Lie algebras; Lie groups; graph theory; image matching; image retrieval; matrix algebra; pattern clustering; quadratic programming; CVP; Lie algebra vector space; common visual pattern discovery; geometric deformation; graph-based quadratic optimization approache; matrix Lie group structure; near-duplicate image retrieval; non-Euclidean nature; nonlinear mean shift clustering; photometric deformation; Algebra; Clustering algorithms; Detectors; Feature extraction; Manifolds; Noise; Visualization; Common pattern discovery; local affine region; mean-shift clustering; near-duplicate image retrieval;
fLanguage :
English
Journal_Title :
Image Processing, IEEE Transactions on
Publisher :
ieee
ISSN :
1057-7149
Type :
jour
DOI :
10.1109/TIP.2015.2481701
Filename :
7274720
Link To Document :
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