Name
Abstract | ||
|---|---|---|
We introduce two appearance-based methods for clustering a set of images of 3-D objects, acquired under varying illumination conditions, into disjoint subsets corresponding to individual objects. The first algorithm is based on the concept of illumination cones. According to the theory, the clustering problem is equivalent to finding convex polyhedral cones in the high-dimensional image space. To efficiently determine the conic structures hidden in the image data, we introduce the concept of conic affinity which measures the likelihood of a pair of images belonging to the same underlying polyhedral cone. For the second method, we introduce another affinity measure based on image gradient comparisons. The algorithm operates directly on the image gradients by comparing the magnitudes and orientations of the image gradient at each pixel. Both methods have clear geometric motivations, and they operate directly on the images without the need for feature extraction or computation of pixel statistics. We demonstrate experimentally that both algorithms are surprisingly effective in clustering images acquired under varying illumination conditions with two large, well-known image data sets. |
Year | DOI | Venue |
|---|---|---|
2003 | 10.1109/cvpr.2003.1211332 | CVPR (1) |
Keywords | Field | DocType |
high-dimensional image space,image gradient comparison,clustering image,image data,illumination cone,clustering appearance,well-known image data set,image gradient,varying illumination condition,affinity measure,clustering problem,clustering algorithms,computer vision,computer science,pixel,statistics,three dimensional,image segmentation,feature extraction,lighting | Computer vision,Image gradient,Data set,Disjoint sets,Pattern recognition,Computer science,Feature extraction,Image segmentation,Artificial intelligence,Pixel,Conic section,Cluster analysis | Conference |
ISSN | ISBN | Citations |
1063-6919 | 0-7695-1900-8 | 250 |
PageRank | References | Authors |
13.57 | 20 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jeffrey Ho | 1 | 2190 | 101.78 |
| Yang Ming-Hsuan | 2 | 15303 | 620.69 |
| Jongwoo Lim | 3 | 4105 | 144.58 |
| Kuang-chih Lee | 4 | 2297 | 104.80 |
| David Kriegman | 5 | 7693 | 451.96 |