Hierarchical Chamfer Matching: A Parametric Edge Matching Algorithm
IEEE Transactions on Pattern Analysis and Machine Intelligence
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Representation and recognition in vision
Representation and recognition in vision
Edge Detection and Ridge Detection with Automatic Scale Selection
International Journal of Computer Vision
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Shape Similarity Measure Based on Correspondence of Visual Parts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A vector space model for automatic indexing
Communications of the ACM
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
The Influence of the gamma-Parameter on Feature Detection with Automatic Scale Selection
Scale-Space '01 Proceedings of the Third International Conference on Scale-Space and Morphology in Computer Vision
Text classification using string kernels
The Journal of Machine Learning Research
Investigating Hidden Markov Models' Capabilities in 2D Shape Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Shapes by Editing Their Shock Graphs
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning to Detect Objects in Images via a Sparse, Part-Based Representation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning Chance Probability Functions for Shape Retrieval or Classification
CVPRW '04 Proceedings of the 2004 Conference on Computer Vision and Pattern Recognition Workshop (CVPRW'04) Volume 6 - Volume 06
Hierarchical Part-Based Visual Object Categorization
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Classification of Contour Shapes Using Class Segment Sets
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Shape Classification Using the Inner-Distance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Shape Recognition and Retrieval Using String of Symbols
ICMLA '06 Proceedings of the 5th International Conference on Machine Learning and Applications
Edit distance-based kernel functions for structural pattern classification
Pattern Recognition
Robust symbolic representation for shape recognition and retrieval
Pattern Recognition
Multiscale Categorical Object Recognition Using Contour Fragments
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Unified Curvature Definition for Regular, Polygonal, and Digital Planar Curves
International Journal of Computer Vision
A similarity-based approach for shape classification using Aslan skeletons
Pattern Recognition Letters
Measuring linearity of closed curves and connected compound curves
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part III
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In this paper, we propose a fragment-based approach for classification and recognition of shape contours. According to this method, first the perceptual landmarks along the contours are localized in a scale invariant manner, which makes it possible to extracts the contour fragments. Using a predefined dictionary for the fragments, these landmarks and the parts between them are transformed into a symbolic representation that is a compact representation. Using a string kernel-like approach, an invariant high-dimensional feature space is created from the symbolic representation and later the most relevant lower dimensions are extracted by principal component analysis. Finally, support vector machine is used for classification of the feature space. The experimental results show that the proposed method has similar performance to the best approaches for shape recognitions while it has lower complexity.