Using the Inner-Distance for Classification of Articulated Shapes

We propose using the inner-distance between landmark points to build shape descriptors. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that the innerdistance is articulation insensitive and more effective at capturing complex shapes with part structures than Euclidean distance. To demonstrate this idea, it is used to build a new shape descriptor based on shape contexts. After that, we design a dynamic programming based method for shape matching and comparison. We have tested our approach on a variety of shape databases including an articulated shape dataset, MPEG7 CE-Shape-1, Kimia silhouettes, a Swedish leaf database and a human motion silhouette dataset. In all the experiments, our method demonstrates effective performance compared with other algorithms.

• Type: Conference
• Year: 2005
• Where: CVPR
• Authors: Haibin Ling, David W. Jacobs
• Keywords: Articulated Shape Dataset, Computer Vision, CVPR 2005, Shape Contexts, Shape Descriptors, Shape Matching, Shape Silhouette