Fast shape index framework based on principle component analysis using edge co-occurrence matrix

Authors:
Zhiping Xu;Yiping Zhong;Shiyong Zhang
Affiliations:
Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China;Department of Computing and Information Technology, Fudan University, Shanghai, China
Venue:
KES'06 Proceedings of the 10th international conference on Knowledge-Based Intelligent Information and Engineering Systems - Volume Part III
Year:
2006

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Using Pen-Based Outlines for Object-Based Annotation and Image-Based Queries

VISUAL '99 Proceedings of the Third International Conference on Visual Information and Information Systems
A novel fusion approach to content-based image retrieval

Pattern Recognition

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Abstract

The shape of an object is one of the most important features in content based image retrieval. However, the statistical feature of edge is rarely used as a feature that codes local spatial information. This paper presents an approach to represent spatial edge distributions using principal component analysis (PCA) on the edge co-occurrence matrix (ECM). The ECM is based on the statistical feature attained from the edge detection operators which applied on the image. The eigenvectors obtained from PCA of the ECM can preserve the high spatial frequencies components, so they are well suited for shape as well as texture representation. Projections of the ECM from the image database to the local PCs serve as a compact representation for the search database. The framework presented in the paper grantee the accuracy and speed of the content based image retrieval in our work.