Shape Matching Using Points Co-occurrence Pattern

Shape matching is a very critical problem in computer vision, and many smart features have been designed in recent literature for improving the similarity measure between pairs of shapes, and most of them consider either distribution of the sample contour points, or convexity/concavity property of the contour. In this paper, we design a novel shape feature to capture the Co-Occurrence Pattern (COP) of the points sampled from any given shape contour, and each pattern is described by \textbf{Self-Similarity} which investigates the spatial co-occurrence relation among all the sample points. We test our feature on three famous shape databases: MPEG-7 CE-Shape-1 part B, Tari1000, and Kimia99 data set for shape matching and retrieval. The experimental results show that the proposed descriptor achieves higher computational efficiency with no significant performance loss.