In this paper, we present an efficient multi-scale similarity matching method for shape-based image indexing and retrieval. This method is affine-invariant and stable against noise and shape deformations. Shapes which have undergone mirror reflection can also be retrieved in a unified manner. In this approach, similarity matching is cast as correspondence matching of two shapes which is then solved by minimizing the matching errors between two feature vectors. Since our feature vectors simultaneously capture both local and global affine-invariant features of shapes, this formulation makes our solution to the correspondence problem very robust. To render the technique suitable for interactive image retrieval, a fast error minimization algorithm for computing correspondence matching is further proposed. Theoretical analysis and experimental results show that multi-scale similarity matching allows dissimilar shapes to be filtered out very quickly and the resulting method meets the performance and flexibility needed for content-based image indexing and retrieval.
ASJC Scopus subject areas
- Signal Processing
- Computer Vision and Pattern Recognition