A Graduated Assignment Algorithm for Graph Matching
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Matching With a Dual-Step EM Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition from Local Scale-Invariant Features
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Distinctive Image Features from Scale-Invariant Keypoints
International Journal of Computer Vision
Histograms of Oriented Gradients for Human Detection
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Efficient Maximally Stable Extremal Region (MSER) Tracking
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
Matching by Linear Programming and Successive Convexification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Harris interest points and the SIFT descriptor for fast scale-invariant object recognition
IROS'09 Proceedings of the 2009 IEEE/RSJ international conference on Intelligent robots and systems
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In this paper, we present a novel object matching approach using the method considering both the similarity on regions and structure in its feature space. The previous works [1], [2] and [3] show that it's possible to formulate the object matching problem as a linear programming problem. However, it remains an open problem how to better use the feature similarity and structure similarity at a same time. In our approach, the contour of the regions of the objects as well as the local structures of these regions in its feature space are considered as two parts of the object matching problem. Hongsheng, Li et al proposed an efficient way to solve these object matching problem by reducing the problem to a linear programming problem.