10 April 2018 Visual texture perception via graph-based semi-supervised learning
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Proceedings Volume 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017); 106153R (2018) https://doi.org/10.1117/12.2302686
Event: Ninth International Conference on Graphic and Image Processing, 2017, Qingdao, China
Abstract
Perceptual features, for example direction, contrast and repetitiveness, are important visual factors for human to perceive a texture. However, it needs to perform psychophysical experiment to quantify these perceptual features’ scale, which requires a large amount of human labor and time. This paper focuses on the task of obtaining perceptual features’ scale of textures by small number of textures with perceptual scales through a rating psychophysical experiment (what we call labeled textures) and a mass of unlabeled textures. This is the scenario that the semi-supervised learning is naturally suitable for. This is meaningful for texture perception research, and really helpful for the perceptual texture database expansion. A graph-based semi-supervised learning method called random multi-graphs, RMG for short, is proposed to deal with this task. We evaluate different kinds of features including LBP, Gabor, and a kind of unsupervised deep features extracted by a PCA-based deep network. The experimental results show that our method can achieve satisfactory effects no matter what kind of texture features are used.
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Qin Zhang, Junyu Dong, Guoqiang Zhong, "Visual texture perception via graph-based semi-supervised learning", Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106153R (10 April 2018); doi: 10.1117/12.2302686; https://doi.org/10.1117/12.2302686
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