A new approach to the maximum-flow problem
Journal of the ACM (JACM)
Interactive Organ Segmentation Using Graph Cuts
MICCAI '00 Proceedings of the Third International Conference on Medical Image Computing and Computer-Assisted Intervention
Multi-camera Scene Reconstruction via Graph Cuts
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Markov Random Fields with Efficient Approximations
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
What Energy Functions Can Be Minimizedvia Graph Cuts?
IEEE Transactions on Pattern Analysis and Machine Intelligence
An Experimental Comparison of Min-Cut/Max-Flow Algorithms for Energy Minimization in Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Remote Sensing and Image Interpretation
Remote Sensing and Image Interpretation
Graph cuts approach to MRF based linear feature extraction in satellite images
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
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In this paper, an unsupervised classification technique is proposed for high resolution satellite imagery. The approach uses graph cuts to improve the k-means algorithm, as graph cuts introduce spatial domain information of the image that is lacking in the k-means. High resolution satellite imagery, IKONOS, and SPOT-5 have been evaluated by the proposed method, showing that graph cuts improve k-means results, which in turn show coherent and continually spatial cluster regions that could be useful for cartographic classification.