Similarity retrieval of occluded shapes using wavelet-based shape features

In this paper, we present a novel approach for describing and estimating similarity of shapes. The target application is content-based indexing and retrieval over large image databases. The shape feature vector is based on the efficient indexing of high curvature (HCP) points which are detected at different levels of resolution of the wavelet transform modulus maxima decomposition. The scale information, together with other topological information of those high curvature points are employed in a sophisticated similarity algorithm. The experimental results and comparisons show that the technique isolates efficiently similar shapes from a large database and reflects adequately the human similarity perception. The proposed algorithm also proved efficient in matching heavily occluded contours with their originals and with other shape contours in the database containing similar portions.