Name
Abstract | ||
|---|---|---|
The mean shift algorithm is a widely used non-parametric clustering algorithm. It has been extended to cluster a mixture of linear subspaces for solving problems in computer vision such as multi-body motion segmentation, etc. Existing methods only work with a set of subspaces, which are computed from samples of observations. However, noises from observations can distort these subspace estimates and influence clustering accuracy. We propose to use both subspaces and observations to improve performance. Furthermore, while these mean shift methods use fixed metrics for computing distances, we prefer an adaptive distance measure. The insight is, we can use temporary modes in a mode seeking process to improve this measure and obtain better performance. In this paper, an adaptive mode seeking algorithm is proposed for clustering linear subspaces. By experiments, the proposed algorithm compares favorably to the state-of-the-art algorithm in terms of clustering accuracy. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33885-4_22 | ECCV Workshops (3) |
Keywords | Field | DocType |
mean shift method,mean shift algorithm,state-of-the-art algorithm,adaptive mode,non-parametric clustering algorithm,better performance,proposed algorithm,linear subspaces,clustering accuracy,adaptive distance measure | k-medians clustering,Fuzzy clustering,Canopy clustering algorithm,CURE data clustering algorithm,Data stream clustering,Pattern recognition,Correlation clustering,Computer science,Artificial intelligence,Mean-shift,Cluster analysis | Conference |
Volume | ISSN | Citations |
7585 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 9 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Guodong Pan | 1 | 1 | 1.38 |
| Lifeng Shang | 2 | 485 | 30.96 |
| Dirk Schnieders | 3 | 44 | 3.93 |
| Shu-Fai Wong | 4 | 64 | 5.06 |