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.
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© 2006 Springer-Verlag Berlin Heidelberg
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Xiang, S., Nie, F., Zhang, C. (2006). Texture Image Segmentation: An Interactive Framework Based on Adaptive Features and Transductive Learning. In: Narayanan, P.J., Nayar, S.K., Shum, HY. (eds) Computer Vision – ACCV 2006. ACCV 2006. Lecture Notes in Computer Science, vol 3851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11612032_23
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DOI: https://doi.org/10.1007/11612032_23
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-31219-2
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