Shape indexing by multi-scale representation
Accessing large image databases requires effective indexing in order to restrict the number of database items that have to be processed. Indexing based on shapes is particularly challenging owing to the difficulty of deriving a similarity measure that supports clustering of shapes conforming with human perceptual similarity. Most previous techniques are based on the extraction of salient shape features and their organization into multi-dimensional point access structures. However, these features are extracted by analyzing shapes at a single resolution scale, and are not able to provide a robust representation. In this paper, we present a technique which exploits 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. A graph structure is introduced to represent shape parts at different scales and a procedure is defined to merge graphs of different shapes. Given a query shape, the graph can be traversed to select, through a coarse to fine matching, those database shapes which share similar structural parts with the query.
shape indexing image database shape representation multi-resolution analysis scalespace filtering