title: Texture classification by texton: statistical versus binary.
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.
authors: Shenzhen Key Laboratory of Broadband Network & Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.
authors: Zhenhua
authors: Z
authors: Guo
authors: Shenzhen Key Laboratory of Broadband Network & Multimedia, Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.
authors: Zhongcheng
authors: Z
authors: Zhang
authors: Graduate School at Shenzhen, Tsinghua University, Shenzhen, China.
authors: Xiu
authors: X
authors: Li
authors: College of Software, Shenzhen Institute of Information Technology, Shenzhen, China.
authors: Qin
authors: Q
authors: Li
authors: Department of Computing, The Hong Kong Polytechnic University, Hong Kong, China.
authors: Jane
authors: J
authors: You
mesh_terms: D000465:Algorithms; D019992:Databases as Topic; D017709:Fractals; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D010363:Pattern Recognition, Automated / Q000379:methods*; D013997:Time Factors