Indexed Retrieval by Shape Appearance

Efficient retrieval by content of visual information requires that visual content descriptors and similarity models are combined with efficient index structures. This problem is particularly challenging in the case of retrieval by shape similarity. The paper discusses retrieval by shape similarity, using local features and effective indexing. Shapes are partitioned into tokens following curvature analysis and each token is modelled by a set of perceptually salient attributes. Two distinct distance functions are used to model token similarity and shape similarity. Shape indexing is obtained by arranging tokens into an M-tree index structure. Examples from a prototype system and computational experiences are reported for both retrieval accuracy and indexing efficiency.