Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape retrieval using triangle-area representation and dynamic space warping
Pattern Recognition
Geometry-Based Image Retrieval in Binary Image Databases
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Rigidity, Cyclic Belief Propagation, and Point Pattern Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Dissimilarity between two skeletal trees in a context
Pattern Recognition
Object recognition using junctions
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
Co-transduction for shape retrieval
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
A multiscale representation method for nonrigid shapes with a single closed contour
IEEE Transactions on Circuits and Systems for Video Technology
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Shape matching/recognition is a very critical problem in the field of computer vision, and a lot of descriptors and methods have been studied in the literature. However, based on predefined descriptors, most of current matching stages are accomplished by finding the optimal correspondence between every two contour points, i.e., in a pair-wised manner. In this paper, we provide a novel matching method which is to find the correspondence between groups of contour points. The points in the same group are adjacent to each other, resulting in a strong relationship among them. Two groups are considered to be matched when the two point sequences formed by the two groups lead to a perfect one-to-one mapping. The proposed group-wised matching method is able to obtain a more robust matching result, since the co-occurrence (order) information of the grouped points is used in the matching stage. We test our method on three famous benchmarks: MPEG-7 data set, Kimia's data set and Tari1000 data set. The retrieval results show that the new group-wised matching method is able to get encouraging improvements compared to some traditional pair-wised matching approaches.