A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
Texture Features for Browsing and Retrieval of Image Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining labeled and unlabeled data with co-training
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Filtering for Texture Classification: A Comparative Study
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Textons, Contours and Regions: Cue Integration in Image Segmentation
ICCV '99 Proceedings of the International Conference on Computer Vision-Volume 2 - Volume 2
Segmentation Given Partial Grouping Constraints
IEEE Transactions on Pattern Analysis and Machine Intelligence
"GrabCut": interactive foreground extraction using iterated graph cuts
ACM SIGGRAPH 2004 Papers
ACM SIGGRAPH 2004 Papers
Semi-supervised learning with graphs
Semi-supervised learning with graphs
Topographic Independent Component Analysis
Neural Computation
Feature synthesized EM algorithm for image retrieval
ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP)
Exploiting intensity inhomogeneity to extract textured objects from natural scenes
ACCV'09 Proceedings of the 9th Asian conference on Computer Vision - Volume Part III
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Texture segmentation is a long standing problem in computer vision. In this paper, we propose an interactive framework for texture segmentation. Our framework has two advantages. One is that the user can define the textures to be segmented by labelling a small part of points belonging to them. The other is that the user can further improve the segmentation quality through a few interactive manipulations if necessary. The filters used to extract the features are learned directly from the texture image to be segmented by the topographic independent component analysis. Transductive learning based on spectral graph partition is then used to infer the labels of the unlabelled points. Experiments on many texture images demonstrate that our approach can achieve good results.