Local Invariant Shape Feature for Cartoon Image Retrieval

Abstract

In this paper, we propose a new method for cartoon image retrieval based on the local invariant shape feature, named Scalable Shape Context. The proposed feature uses the Harris-Laplace corner to localize the key points and corresponding scale in the cartoon image. Then, we use Shape Context to describe the local shape. The feature point matching is achieved by a weighted bipartite graph matching algorithm and the similarity between the query and the indexing image is presented by the match cost. The experimental results show that our method is more efficient than Shape Context and SIFT for the cartoon image retrieval.