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Title: Texture classification by texton : statistical versus binary
Authors: Guo, Z
Zhang, Z
Li, X
Li, Q
You, J 
Issue Date: 2014
Publisher: Public Library of Science
Source: PLoS one, 2014, v. 9, no. 2, e88073, p. 1-13 How to cite?
Journal: PLoS one 
Abstract: Using statistical textons for texture classification has shown great success recently. The maximal response 8 (Statistical-MR8), image patch (Statistical-Joint) and locally invariant fractal (Statistical-Fractal) are typical statistical texton algorithms and state-of-the-art texture classification methods. However, there are two limitations when using these methods. First, it needs a training stage to build a texton library, thus the recognition accuracy will be highly depended on the training samples; second, during feature extraction, local feature is assigned to a texton by searching for the nearest texton in the whole library, which is time consuming when the library size is big and the dimension of feature is high. To address the above two issues, in this paper, three binary texton counterpart methods were proposed, Binary-MR8, Binary-Joint, and Binary-Fractal. These methods do not require any training step but encode local feature into binary representation directly. The experimental results on the CUReT, UIUC and KTH-TIPS databases show that binary texton could get sound results with fast feature extraction, especially when the image size is not big and the quality of image is not poor.
EISSN: 1932-6203
DOI: 10.1371/journal.pone.0088073
Rights: © 2014 Guo et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License (, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
The following publication Guo, Z., Zhang, Z., Li, X., Li, Q., & You, J. (2014). Texture classification by texton: Statistical versus binary. PLoS ONE, 9(2), e88073, 1-13 is available at
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