Perceptually motivated morphological strategies for shape retrieval

In this paper, two perceptually motivated morphological strategies (PMMS) are proposed to enhance the retrieval performance of common shape matching methods. Firstly, two human perception customs are introduced, which have important relations to shape retrieval. Secondly, these two customs are properly modeled by morphological operations. Finally, the proposed PMMS is applied to improve the retrieval performances of a popular shape matching method named Inner-Distance Shape Contexts (IDSC), and then the Locally Constrained Diffusion Process (LCDP) method is exploited to further enhance the retrieval performance. This combination achieves a retrieval rate of 98.56% on MPEG-7 dataset. We also conduct the experiments on Swedish Leaf dataset, the ETH-80 dataset and the Natural Silhouette dataset. The experimental results obtained from four datasets demonstrate clearly the effectiveness of the proposed method.

► We propose two human perception customs that should be considered in shape retrieval application.
► We prove that using morphological strategies could simulate these two customs properly.
► We compare two morphological strategies and determine the better one.
► We combine our method with the existing methods and achieve the state-of-the-art performance on MPEG-7 database.