Retrieval by Shape Similarity with Perceptual Distance and Effective Indexing
An important problem in accessing and retrieving visual information is to provide efficient similarity matching in large databases. Though much work is being done on the investigation of suitable perceptual models and the automatic extraction of features, little attention is given to the combination of useful representations and similarity models with efficient index structures. In this paper we propose retrieval by shape similarity using local descriptors and effective indexing. Shapes are partitioned into tokens in correspondence with their protrusions, and each token is modeled according to a set of perceptually salient attributes. Shape indexing is obtained by arranging shape tokens into a suitably modified M-tree index structure. Two distinct distance functions model respectively, token and shape perceptual similarity. Examples from a prototype system and computational experiences are reported for both retrieval accuracy and indexing efficiency. Shape retrieval has been tested under shape scaling, orientation changes, and partial shape occlusions. A comparative analysis of different indexing structures, for shape retrieval is presented.