Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mean Shift: A Robust Approach Toward Feature Space Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Segmentation by Data-Driven Markov Chain Monte Carlo
IEEE Transactions on Pattern Analysis and Machine Intelligence
Contour and Texture Analysis for Image Segmentation
International Journal of Computer Vision
Multiclass Spectral Clustering
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Learning a Classification Model for Segmentation
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Efficient Graph-Based Image Segmentation
International Journal of Computer Vision
Spectral Segmentation with Multiscale Graph Decomposition
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
Scale-Invariant Contour Completion Using Conditional Random Fields
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Guiding Model Search Using Segmentation
ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Learning Probabilistic Models for Contour Completion in Natural Images
International Journal of Computer Vision
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
What Is a Good Image Segment? A Unified Approach to Segment Extraction
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part IV
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Natural image segmentation with adaptive texture and boundary encoding
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part I
Segmentation of Multivariate Mixed Data via Lossy Data Coding and Compression
IEEE Transactions on Pattern Analysis and Machine Intelligence
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We investigate a fundamental problem in computer vision: unsupervised image segmentation. During the last decade, the Normalized Cuts has become very popular for image segmentation. NCuts guarantees a globally optimal solution in the continuous solution space, however, how to automatically select the number of segments for a given image is left as an open problem. Recently, the lossy minimum description length (LMDL) criterion has been proposed for segmentation of images. This criterion can adaptively determine the number of segments, however, as the optimization is combinatorial, only a suboptimal solution can be achieved by a greedy algorithm. The complementarity of both criteria motivates us to combine NCuts and LMDL into a unified fashion, to achieve a better segmentation: given the NCuts segmentations under different numbers of segments, we choose the optimal segmentation to be the one that minimizes the overall coding length, subject to a given distortion. We then develop a new way to use the coding length decrement as the similarity measure for NCuts, so that our algorithm is able to seek both the optimal NCuts solution under fixed number of segments, and the optimal LMDL solution among different numbers of segments. Extensive experiments demonstrate the effectiveness of our algorithm.