A Shape Clustering Based Framework for Fast Context-Sensitive Shape Retrieval

Shape retrieval is still a challenging problem. To address this problem, there is a growing interest in context-sensitive shape retrieval. In such methods, when computing the similarity between any two shape objects, the influence of their neighbors is propagated by a diffusion process on contextual space. We can re-evaluate the pairwise similarities to get better retrieval results. Although the context-sensitive shape retrieval methods are very promising in theory, but the time efficiency of these methods is a big problem in practice. Because it is a time-consuming to diffuse on the contextual space formed by all shape objects in the database. To improve the time efficiency, we propose a novel framework for context-sensitive shape retrieval. This framework consists of the off-line and the on-line stages. In the off-line stage, we conduct context-sensitive based shape clustering to build index. In the on-line stage, we quickly find candidate shape objects for the query by the index. Then, by diffusion on the context space consisting of candidate objects, we can get the final retrieval results. Compared with the state-of-the-art methods, the proposed framework achieves the same bull´s-eye retrieval score with more than 80% reduction of searching time on the standard shape database.