Scale-Based Description and Recognition of Planar Curves and Two-Dimensional Shapes
IEEE Transactions on Pattern Analysis and Machine Intelligence
Algorithms for clustering data
Algorithms for clustering data
On Image Analysis by the Methods of Moments
IEEE Transactions on Pattern Analysis and Machine Intelligence
Symbolic clustering using a new dissimilarity measure
Pattern Recognition
Shapes Recognition Using the Straight Line Hough Transform: Theory and Generalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object recognition by a linear weight classifier
Pattern Recognition Letters
Agglomerative clustering of symbolic objects using the concepts of both similarity and dissimilarity
Pattern Recognition Letters
Effective classification of planar shapes based on curve segment properties
Pattern Recognition Letters
Hierarchical representation of 2-D shapes using convex polygons: A morphological approach
Pattern Recognition Letters
Multidimensional scaling of interval-valued dissimilarity data
Pattern Recognition Letters
Using moment invariants and HMM in facial expression recognition
Pattern Recognition Letters
ACCV'06 Proceedings of the 7th Asian conference on Computer Vision - Volume Part II
Concept, content and the convict
MM '09 Proceedings of the 17th ACM international conference on Multimedia
A symbolic approach for text classification based on dissimilarity measure
Proceedings of the First International Conference on Intelligent Interactive Technologies and Multimedia
A novel ring radius transform for video character reconstruction
Pattern Recognition
Text classification using symbolic similarity measure
Proceedings of the Second International Conference on Computational Science, Engineering and Information Technology
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In this paper, we present a method for representing a two-dimensional shape by symbolic features. A shape is represented in terms of multi-interval valued type features. A similarity measure defined over symbolic features that is useful for retrieval of shapes from a shape database is also presented. Unlike other shape representation schemes, the proposed scheme is capable of preserving both contour as well as region information. The proposed method of shape representation and retrieval is shown to be invariant to image transformations (translation, rotation, reflection and scaling) and robust to minor deformations and occlusions. Several experiments have been conducted to demonstrate the feasibility of the methodology and also to highlight its advantages over an existing methodology.