Invariant-based shape retrieval in pictorial databases

One of the strongest cues for retrieval of content information from images is shape. However, due to the wide range of transformations that an object might undergo, this is also the most difficult one to handle. It seems that shape retrieval is one of the major barriers nowadays on the way of image databases to become commonly used. Common approaches use global attributes (Faloutsos et al. [1]), feature points (Pentland et al. [2]), histograms (Jain and Vailaya [3]), or physical models of deformations (Del Bimbo and Pala [4]). We present an approach for shape retrieval from pictorial databases which is based on invariant features of the image. In particular we use a combination of semi-local multi-valued invariant signatures and global features. Spatial relations and global properties are used to eliminate non-relevant images before similarity is computed. Common approaches usually don't handle viewpoint transformations more complex than similarity and require the full shape in order to compute image features. The advantages of the proposed approach are its ability to handle images distorted by different viewpoint transformations, its ability to retrieve images even in situations in which part of the shape is missing (i.e., in case of occlusion or sketch-based queries), and its ability to support efficient indexing.