Planar radon based shape transforms for features classification in multi-dimensional space

Abstract

Till now, shape based retrieval (SBR) systems exploit spatial features. None of the available systems combines all features, texture, and shape for retrieval. Moreover, relatively few systems use Radon transform in texture extraction features, despite the widely acclaimed efficiency. This paper proposes a novel feature-based shape descriptors using Radon composite features by using statistical analysis, instead of analyzing shapes directly in the spatial domain. The proposed system uses combination of integrated first and second moments of radon transformed image features, and Hue Moments features of the regions as shape features, then linear discriminant analysis (LDA) is applied for decreasing the dimension of feature vector and non-linear combination of vector dimensions for generating optimum features. Experiments demonstrate that proposed novel feature-based shapes system provides a higher degree of retrieval and are compared with several state-of-the-art approaches.

Keywords: Shape classification, descriptor, retrieval, radon transform, linear discriminant analysis.