Abstract 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.
Texture Image Segmentation: An Interactive Framework Based on Adaptive Features and Transductive Learning
Computer Vision – ACCV 2006 ; 4 ; 216-225
Lecture Notes in Computer Science ; 3851 , 4
2006-01-01
10 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Texture Feature , Independent Component Analysis , Texture Image , Texture Segmentation Computer Science , Computer Imaging, Vision, Pattern Recognition and Graphics , Pattern Recognition , Image Processing and Computer Vision , Artificial Intelligence (incl. Robotics) , Algorithm Analysis and Problem Complexity
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