Visual texture perception via graph-based semi-supervised learning

Qin Zhang; Junyu Dong; Guoqiang Zhong

doi:10.1117/12.2302686

10 April 2018 Visual texture perception via graph-based semi-supervised learning

Qin Zhang, Junyu Dong, Guoqiang Zhong

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.

Citation Download Citation

Qin Zhang, Junyu Dong, and 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); https://doi.org/10.1117/12.2302686

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KEYWORDS

Visualization

Feature extraction

Solid state lighting

Data modeling

Databases

Binary data

Data processing

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