Laplacian Eigenmaps for dimensionality reduction and data representation
Neural Computation
Random Walks for Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Closed-Form Solution to Natural Image Matting
IEEE Transactions on Pattern Analysis and Machine Intelligence
Object Recognition by Integrating Multiple Image Segmentations
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part III
Learning CRFs Using Graph Cuts
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part II
Graph cut based inference with co-occurrence statistics
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part V
On parameter learning in CRF-based approaches to object class image segmentation
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part VI
Towards a theoretical foundation for laplacian-based manifold methods
COLT'05 Proceedings of the 18th annual conference on Learning Theory
O(N) implicit subspace embedding for unsupervised multi-scale image segmentation
CVPR '11 Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition
Graph Laplacian Tomography From Unknown Random Projections
IEEE Transactions on Image Processing
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In this paper we formulate the task of semantic image segmentation as a manifold embedding problem and solve it using graph Laplacian approximation. This allows for unsupervised learning of graph Laplacian parameters individually for each image without using any prior information. We perform experiments on GrabCut, Graz and Pascal datasets. At a low computational cost proposed learning method shows comparable performance to choosing the parameters on the test set. Our framework for semantic image segmentation shows better performance than the standard discrete CRF with graph-cut inference.