Efficient shape representation and retrieval in large database

Most of the existing shape retrieval methods need a one-to-one shape descriptor matching procedure to achieve a high retrieval rate. However, high performance shape matching methods are usually computationally demanding, which are obviously not suitable for large shape databases. Shapes should be indexed for efficient retrieval. In this paper, we propose a simple but efficient shape descriptor ROMS and index shapes via the Bag-of-Words (BoW) framework. ROMS is a multi-scale descriptor and defined by the ratio of a triangle middle and side line in each scale. In order to deal with articulation, part-aware metric is also introduced. These strategies make ROMS invariant to translation, rotation, scale, articulation, meanwhile capturing both the local curvature information and the part structure of the shape. Furthermore, we present a symmetry detection method based on ROMS. Owing to the above distinguishing characteristics and advantages, the method can detect both extrinsic and intrinsic symmetries. Extensive experiments have been performed on several public databases including the MPEG7 CE-shape-1, the Kimia database and the ETH-80 database. The experiments show that ROMS achieves better result than the state of art methods and scales up to large database via BoW framework.