Grassmann manifold based shape matching and retrieval under partial occlusions

Chenxi Li; Zelin Shi; Yunpeng Liu; Baoshu Xu

doi:10.1117/12.2072864

24 November 2014 Grassmann manifold based shape matching and retrieval under partial occlusions

Chenxi Li, Zelin Shi, Yunpeng Liu, Baoshu Xu

Proceedings Volume 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition; 93012O (2014) https://doi.org/10.1117/12.2072864
Event: International Symposium on Optoelectronic Technology and Application 2014, 2014, Beijing, China

Abstract

Shape matching and recognition is a challenging task due to geometric distortions and occlusions. A novel shape matching approach based on Grassmann manifold is proposed that affine transformations and partial occlusions are both considered. An affine invariant Grassmann shape descriptor is employed which projects one shape contour to a point on Grassmann manifold and gives the similarity measurement between two contours based on the geodesic distance on the manifold. At first, shape contours are parameterized by affine length and accordingly divided into local affine-invariant shape segments, which are represented by the Grassmann shape descriptor, according to their curvature scale space images. Then the Smith-Waterman algorithm is employed to find the common parts of two shapes’ segment sequences, and get the local similarity of shapes. The global similarity is given by the found common parts, and finally the shape recognition accomplished by the weighted sum of local similarity and global similarity. The robustness of the Grassmann shape descriptor is analyzed through subspace perturbation analysis theory. Retrieval experiments show that our approach is effective and robust under affine transformations and partial occlusions.

Citation Download Citation

Chenxi Li, Zelin Shi, Yunpeng Liu, and Baoshu Xu "Grassmann manifold based shape matching and retrieval under partial occlusions", Proc. SPIE 9301, International Symposium on Optoelectronic Technology and Application 2014: Image Processing and Pattern Recognition, 93012O (24 November 2014); https://doi.org/10.1117/12.2072864

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