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Mehdi Chehel Amirani, Zahra Sadeghi Gol, Ali Asghar Beheshti Shirazi


Content-based image retrieval (CBIR) is very active research topic in recent years. This paper introduces a new approach to shape-based image retrieval. At first, feature points are determined at the boundary of the shape as the extremums of a new version of the curvature function and the initial features are calculated at these points. The proposed method utilizes a supervised system for nonlinear combination of initial features for extraction of efficient and low dimensional feature vector for each shape. The retrieval performance of the approach is illustrated using the MPEG-7 shape database. Our experiments show that the proposed method is well suited for object indexing and retrieval in large databases.


Principle Component Analysis; Linear Discriminant Analysis; Neural Networks; Feature Extraction; Curvature Function; Image Retrieval

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