International Journal of Computer Vision
Normalized Cuts and Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fast Approximate Energy Minimization via Graph Cuts
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
Yet Another Survey on Image Segmentation: Region and Boundary Information Integration
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part III
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
Seeded region growing: an extensive and comparative study
Pattern Recognition Letters
Comparing clusterings: an axiomatic view
ICML '05 Proceedings of the 22nd international conference on Machine learning
Graph Cuts and Efficient N-D Image Segmentation
International Journal of Computer Vision
Toward Objective Evaluation of Image Segmentation Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised segmentation of natural images via lossy data compression
Computer Vision and Image Understanding
Image segmentation evaluation: A survey of unsupervised methods
Computer Vision and Image Understanding
Pairwise constraint propagation by semidefinite programming for semi-supervised classification
Proceedings of the 25th international conference on Machine learning
ECML PKDD '08 Proceedings of the European conference on Machine Learning and Knowledge Discovery in Databases - Part II
Robust fuzzy clustering-based image segmentation
Applied Soft Computing
Generative Image Segmentation Using Random Walks with Restart
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Linear Neighborhood Propagation and Its Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Interactive image segmentation by maximal similarity based region merging
Pattern Recognition
Automatic seeded region growing for color image segmentation
Image and Vision Computing
Region merging techniques using information theory statistical measures
IEEE Transactions on Image Processing
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
User-friendly interactive image segmentation through unified combinatorial user inputs
IEEE Transactions on Image Processing
Semisupervised kernel matrix learning by kernel propagation
IEEE Transactions on Neural Networks
Image segmentation via coherent clustering in L*a*b* color space
Pattern Recognition Letters
A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging
Journal of Mathematical Imaging and Vision
Color Image Segmentation Based on Mean Shift and Normalized Cuts
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Relay Level Set Method for Automatic Image Segmentation
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Class-Adaptive Spatially Variant Mixture Model for Image Segmentation
IEEE Transactions on Image Processing
Minimization of Region-Scalable Fitting Energy for Image Segmentation
IEEE Transactions on Image Processing
Color clustering and learning for image segmentation based on neural networks
IEEE Transactions on Neural Networks
Automatic Image Segmentation by Dynamic Region Merging
IEEE Transactions on Image Processing
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In this paper, we propose automatic image segmentation using constraint learning and propagation. Recently, kernel learning is receiving much attention because a learned kernel can fit the given data better than a predefined kernel. To effectively learn the constraints generated by initial seeds for image segmentation, we employ kernel propagation (KP) based on kernel learning. The key idea of KP is first to learn a small-sized seed-kernel matrix and then propagate it into a large-sized full-kernel matrix. By applying KP to automatic image segmentation, we design a novel segmentation method to achieve high performance. First, we generate pairwise constraints, i.e., must-link and cannot-link, from initially selected seeds to make the seed-kernel matrix. To select the optimal initial seeds, we utilize global k-means clustering (GKM) and self-tuning spectral clustering (SSC). Next, we propagate the seed-kernel matrix into the full-kernel matrix of the entire image, and thus image segmentation results are obtained. We test our method on the Berkeley segmentation database, and the experimental results demonstrate that the proposed method is very effective in automatic image segmentation.