Shape Classification Using the Inner-Distance

TitleShape Classification Using the Inner-Distance
Publication TypeJournal Articles
Year of Publication2007
AuthorsLing H, Jacobs DW
JournalPattern Analysis and Machine Intelligence, IEEE Transactions on
Pagination286 - 299
Date Published2007/02//
ISBN Number0162-8828
KeywordsAutomated;Reproducibility of Results;Sensitivity and Specificity;, Computer-Assisted;Imaging, Euclidean distance;articulation invariant signatures;computer vision;inner distance;multidimensional scaling;part structure;shape classification;shape descriptors;shape silhouette;shortest path;computational geometry;computer vision;graph theory;image cla, Three-Dimensional;Pattern Recognition

Part structure and articulation are of fundamental importance in computer and human vision. We propose using the inner-distance to build shape descriptors that are robust to articulation and capture part structure. The inner-distance is defined as the length of the shortest path between landmark points within the shape silhouette. We show that it is articulation insensitive and more effective at capturing part structures than the Euclidean distance. This suggests that the inner-distance can be used as a replacement for the Euclidean distance to build more accurate descriptors for complex shapes, especially for those with articulated parts. In addition, texture information along the shortest path can be used to further improve shape classification. With this idea, we propose three approaches to using the inner-distance. The first method combines the inner-distance and multidimensional scaling (MDS) to build articulation invariant signatures for articulated shapes. The second method uses the inner-distance to build a new shape descriptor based on shape contexts. The third one extends the second one by considering the texture information along shortest paths. The proposed approaches have been tested on a variety of shape databases, including an articulated shape data set, MPEG7 CE-Shape-1, Kimia silhouettes, the ETH-80 data set, two leaf data sets, and a human motion silhouette data set. In all the experiments, our methods demonstrate effective performance compared with other algorithms