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Retrieving similar images from a large dataset based on the image content hasbeen a very active research area and is a very challenging task. Studies haveshown that retrieving similar images based on their shape is a very effectivemethod. For this purpose a large number of methods exist in literature. Thecombination of more than one feature has also been investigated for thispurpose and has shown promising results. In this paper a fusion based shapesrecognition method has been proposed. A set of local boundary based and regionbased features are derived from the labeled grid based representation of theshape and are combined with a few global shape features to produce a compositeshape descriptor. This composite shape descriptor is then used in a weightedranking algorithm to find similarities among shapes from a large dataset. Theexperimental analysis has shown that the proposed method is powerful enough todiscriminate the geometrically similar shapes from the non-similar ones.

A Fusion of Labeled-Grid Shape Descriptors with Weighted Ranking Algorithm for Shapes Recognition