Shape classification by manifold learning in multiple observation spaces

Abstract

Manifold learning is a non-linear method with the aim of finding a constructive way to embed the data from a high-dimensional space into a low-dimensional one. The improvement of shape classification is the major approach in this paper which is based on the continuity in feature space in accordance with the continuity in semantic one. In this regard, a non-linear approach is employed to map the shape feature vectors to a new space while their semantics become similar with human opinion, the Euclidean distance between two feature vectors would be closed. Shapes are described by four contour-based and region-based techniques by the proposed method. In other word, they are described in four observation spaces. Furthermore, a parameter space is learnt from multiple observation spaces based on fusion of dissimilarities in a supervised manner. Experimental results show the validity and efficiency of the proposed approach for shape classification over a variety of standard shape datasets.