Geometry-based 2D shape descriptor for retrieval in large database

Abstract

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 by 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 accumulation, part-aware metric is also introduced. These strategies make ROMS invariant to translation, rotation, scale, accumulation, meanwhile capturing both the local curvature information and the part structure of the shape. Extensive experiments have been performed on several public databases including MPEG7 CE-shape-1, Kimia database, the ETH-80 database. The experiments show that ROMS achieves result better than the state of art methods and scales up to large database via BOW framework.