International Journal of Computer Vision
Shock Graphs and Shape Matching
International Journal of Computer Vision
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
State of the art in shape matching
Principles of visual information retrieval
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
Indexing Hierarchical Structures Using Graph Spectra
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Representation and Classification Using the Poisson Equation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Analysis of Two-Dimensional Non-Rigid Shapes
International Journal of Computer Vision
Blur Insensitive Texture Classification Using Local Phase Quantization
ICISP '08 Proceedings of the 3rd international conference on Image and Signal Processing
Disconnected Skeleton: Shape at Its Absolute Scale
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Context-Sensitive Shape Similarity by Graph Transduction
IEEE Transactions on Pattern Analysis and Machine Intelligence
Articulation-invariant representation of non-planar shapes
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Balancing deformability and discriminability for shape matching
ECCV'10 Proceedings of the 11th European conference on computer vision conference on Computer vision: Part III
Matrix representation in pattern classification
Expert Systems with Applications: An International Journal
Shape matching and classification using height functions
Pattern Recognition Letters
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Shape classification is a field of study with applications ranging from object classification to leaf recognition. In this paper we present an approach based on a matrix descriptor, the local phase quantization, for improving the performance of such widely used shape descriptors as the inner distance shape context (ID), shaper context (SC), and height functions (HF). The basic idea of our novel approach is to transform the shape descriptor obtained by ID/SC/HF into a matrix so that matrix descriptors can be extracted. These matrix descriptors are then compared with the Jeffrey distance and combined with standard ID/SC/HF shape similarity. Since it has recently been shown that ID/SC/HF shape similarities can be coupled with learning context-sensitive shape similarity using graph transduction (LGT) for improving the results, we have also coupled our approach with LGT. Our proposed approach is tested on a wide variety of shape databases including MPEG7 CE-Shape-1, Kimia silhouettes, Tari dataset, a leaf dataset, a tools dataset, a myths figures dataset, and our new human dancer dataset. The experimental results demonstrate that the proposed approach yields significant improvements over baseline shape matching algorithms. All Matlab code used in the proposed paper is available at bias.csr.unibo.it/nanni/Shape.rar.