Balancing Deformability and Discriminability for Shape Matching

We propose a novel framework,

aspect space

, to balance deformability and discriminability, which are often two competing factors in shape and image representations. In this framework, an object is embedded as a surface in a higher dimensional space with a parameter named

aspect weight

, which controls the importance of intensity in the embedding. We show that this framework naturally unifies existing important shape and image representations by adjusting the aspect weight and the embedding. More importantly, we find that the aspect weight implicitly controls the degree to which a representation handles deformation. Based on this idea, we present the

aspect shape context

, which extends shape context-based descriptors and adaptively selects the “best” aspect weight for shape comparison. Another observation we have is the proposed descriptor nicely fits context-sensitive shape retrieval. The proposed methods are evaluated on two public datasets, MPEG7-CE-Shape-1 and Tari 1000, in comparison to state-of-the-art solutions. In the standard shape retrieval experiment using the MPEG7 CE-Shape-1 database, the new descriptor with context information achieves a bull’s eye score of 95.96%, which surpassed all previous results. In the Tari 1000 dataset, our methods significantly outperform previous tested methods as well.