Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Pairwise Data Clustering by Deterministic Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Normalized Cuts and Image Segmentation
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Clustering Algorithms and Validity Measures
SSDBM '01 Proceedings of the 13th International Conference on Scientific and Statistical Database Management
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Spectral Grouping Using the Nyström Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Analytical Honeycomb Geometry for Raster and Volume Graphics
The Computer Journal
Image segmentation with ratio cut
IEEE Transactions on Pattern Analysis and Machine Intelligence
Hierarchical vibrations for part-based recognition of complex objects
Pattern Recognition
Turbopixel segmentation using Eigen-images
IEEE Transactions on Image Processing
| Hi-index | 0.00 |
Image segmentation based on pairwise pixel similarities has been a very active field of research in recent years. The drawbacks common to these segmentation methods are the enormous space and processor requirements. The contribution of this paper is a general purpose two-stage preprocessing method that substantially reduces the involved costs. Initially, an oversegmentation into small coherent image patches - or superpixels - is obtained through an iterative process guided by pixel similarities. A suitable pairwise superpixel similarity measure is then defined which may be plugged into an arbitrary segmentation method based on pairwise pixel similarities. To illustrate our ideas we integrated the algorithm into a spectral graph-partitioning method using the Normalized Cut criterion. Our experiments show that the time and memory requirements are reduced drastically ( 99%), while segmentations of adequate quality are obtained.