  Retrieving similar images from a large dataset based on the image content has
been a very active research area and is a very challenging task. Studies have
shown that retrieving similar images based on their shape is a very effective
method. For this purpose a large number of methods exist in literature. The
combination of more than one feature has also been investigated for this
purpose and has shown promising results. In this paper a fusion based shapes
recognition method has been proposed. A set of local boundary based and region
based features are derived from the labeled grid based representation of the
shape and are combined with a few global shape features to produce a composite
shape descriptor. This composite shape descriptor is then used in a weighted
ranking algorithm to find similarities among shapes from a large dataset. The
experimental analysis has shown that the proposed method is powerful enough to
discriminate the geometrically similar shapes from the non-similar ones.
