Shape Indexing by Multi-scale Representation

The availability of large image databases and retrieval by content has imposed the requirement for indexing procedures to allow a fast pruning of the database items. Indexing of shapes is particularly challenging owing to the difficulty to derive a similarity measure that supports clustering of shapes according to human perceptual similarity. In this paper, we present a technique which exploits a multi-scale analysis of shapes, to derive a hierarchical shape representation in which shape details are progressively filtered out while shape characterizing elements are preserved. To provide the necessary degree of robustness with respect to shape variability, fuzzy sets have been used to describe the visual appearance of shape parts. A graph-like index structure is derived by clustering shapes sharing similar part descriptions. Results of indexing for a sample database are reported, with efficiency and effectiveness measures.