Relational histograms for shape indexing

This paper is concerned with the retrieval of images from large databases based on their shape similarity to a query image. Our approach is based on two dimensional histograms that encode both the local and global geometric properties of the shapes. The pairwise attributes are the directed segment relative angle and directed relative position. The novelty of the proposed approach is to simultaneously use the relational and structural constraints, derived from an adjacency graph, to gate histogram contributions. We investigate the retrieval capabilities of the method for various queries. We also investigate the robustness of the method to segmentation errors. We conclude that a relational histogram of pairwise segment attributes presents a very efficient way of indexing into large databases. The optimal configuration is obtained when the local features are constructed from six neighbouring segments pairs. Moreover, a sensitivity analysis reveals that segmentation errors do not affect the retrieval performances.