Abstract
When used for image segmentation, most standard clustering algorithms can shift image boundaries due to intensity fluctuations within an image. In this paper, a novel approach to clustering is proposed for performing unsupervised image segmentation based upon a generalization of the standard K-means clustering algorithm. By incorporating a new term into the objective function of the K-means algorithm, boundaries between regions in the resulting segmentation are forced to occur at the same locations as edges in the observed image. A straightforward iterative algorithm is derived for minimizing this edge-adaptive K-means objective function. The result is an efficient segmentation algorithm that reconstructs boundaries in the image more accurately than standard methods.
Original language | English (US) |
---|---|
Pages | 816-819 |
Number of pages | 4 |
State | Published - Dec 1 2000 |
Event | International Conference on Image Processing (ICIP 2000) - Vancouver, BC, Canada Duration: Sep 10 2000 → Sep 13 2000 |
Other
Other | International Conference on Image Processing (ICIP 2000) |
---|---|
Country/Territory | Canada |
City | Vancouver, BC |
Period | 9/10/00 → 9/13/00 |
ASJC Scopus subject areas
- Hardware and Architecture
- Computer Vision and Pattern Recognition
- Electrical and Electronic Engineering