Texture classification by texton: Statistical versus binary

Zhenhua Guo, Zhongcheng Zhang, Xiu Li, Qin Li, Jia You

Research output: Journal article publicationJournal articleAcademic researchpeer-review

11 Citations (Scopus)

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.
Original languageEnglish
Article numbere88073
JournalPLoS ONE
Volume9
Issue number2
DOIs
Publication statusPublished - 10 Feb 2014

ASJC Scopus subject areas

  • Medicine(all)
  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this