Contour Based Shape Matching for Object Recognition

To improve computational efficiency and solve the problem of low accuracy caused by geometric transformations and nonlinear deformations in the shape-based object recognition, a novel contour signature is proposed. This signature includes five types of invariants in different scales to obtain representative local and semi-global shape features. Then the Dynamic Programming algorithm is applied to shape matching to find the best correspondence between two shape contours. The experimental results validate that our methods is robust to rotation, scaling, occlusion, intra-class variations and articulated variations. Moreover, the superior shape matching and retrieval accuracy on benchmark datasets verifies the effectiveness of our method.

[1]  Zhuowen Tu,et al.  Shape Matching and Recognition - Using Generative Models and Informative Features , 2004, ECCV.

[2]  Jianyu Yang,et al.  Invariant multi-scale descriptor for shape representation, matching and retrieval , 2016, Comput. Vis. Image Underst..

[3]  Xiaojun Wu,et al.  A novel contour descriptor for 2D shape matching and its application to image retrieval , 2011, Image Vis. Comput..

[4]  Xiaoou Tang,et al.  2D Shape Matching by Contour Flexibility , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  Pepe Siy,et al.  Robust shape similarity retrieval based on contour segmentation polygonal multiresolution and elastic matching , 2005, Pattern Recognit..

[6]  Ying Zhang,et al.  Learning context-sensitive similarity by shortest path propagation , 2011, Pattern Recognit..

[7]  Zhuowen Tu,et al.  Learning Context-Sensitive Shape Similarity by Graph Transduction , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Jianyu Yang,et al.  Metric learning based object recognition and retrieval , 2016, Neurocomputing.

[9]  Philip N. Klein,et al.  Recognition of shapes by editing their shock graphs , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[10]  Xiang Bai,et al.  Shape Vocabulary: A Robust and Efficient Shape Representation for Shape Matching , 2014, IEEE Transactions on Image Processing.

[11]  Anuj Srivastava,et al.  Analysis of planar shapes using geodesic paths on shape spaces , 2004, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  Josef Kittler,et al.  Efficient and Robust Retrieval by Shape Content through Curvature Scale Space , 1998, Image Databases and Multi-Media Search.

[13]  Philip N. Klein,et al.  On Aligning Curves , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[14]  Haibin Ling,et al.  Shape Classification Using the Inner-Distance , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Ronald L. Rivest,et al.  Introduction to Algorithms , 1990 .

[16]  Naif Alajlan,et al.  Shape retrieval using triangle-area representation and dynamic space warping , 2007, Pattern Recognit..

[17]  Ulrich Eckhardt,et al.  Shape descriptors for non-rigid shapes with a single closed contour , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[18]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[19]  Jianyu Yang,et al.  Parsing 3D motion trajectory for gesture recognition , 2016, J. Vis. Commun. Image Represent..

[20]  Longin Jan Latecki,et al.  Shape Matching for Robot Mapping , 2004, PRICAI.

[21]  Daniel Cremers,et al.  Integral Invariants for Shape Matching , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Naif Alajlan,et al.  Geometry-Based Image Retrieval in Binary Image Databases , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Hassen Drira,et al.  3D Face Recognition under Expressions, Occlusions, and Pose Variations , 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.