Ownership protection of shape datasets with geodesic distance preservation
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
A Context Dependent Distance Measure for Shape Clustering
ISVC '08 Proceedings of the 4th International Symposium on Advances in Visual Computing, Part II
Maximum normalized spacing for efficient visual clustering
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Beyond pairwise shape similarity analysis
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
Joint view-identity manifold for infrared target tracking and recognition
Computer Vision and Image Understanding
| Hi-index | 0.00 |
Shape clustering can significantly facilitate the automatic labeling of objects present in image collections. For example, it could outline the existing groups of pathological cells in a bank of cyto-images; the groups of species on photographs collected from certain aerials; or the groups of objects observed on surveillance scenes from an office building. Here we demonstrate that a nonlinear projection algorithm such as Isomap can attract together shapes of similar objects, suggesting the existence of isometry between the shape space and a low dimensional nonlinear embedding. Whenever there is a relatively small amount of noise in the data, the projection forms compact, convex clusters that can easily be learned by a subsequent partitioning scheme. We further propose a modification of the Isomap projection based on the concept of degree-bounded minimum spanning trees. The proposed approach is demonstrated to move apart bridged clusters and to alleviate the effect of noise in the data.