Texture classification by texton: statistical versus binary

PLoS One. 2014 Feb 10;9(2):e88073. doi: 10.1371/journal.pone.0088073. eCollection 2014.

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Algorithms*
  • Databases as Topic
  • Fractals
  • Humans
  • Image Interpretation, Computer-Assisted*
  • Pattern Recognition, Automated / methods*
  • Time Factors

Grants and funding

The authors sincerely thank MVG and VGG for sharing the source codes of LBP and Statistical_MR8. The work is partially supported by the Natural Science Foundation of China (NSFC) (number 61101150), Shenzhen special fund for the strategic development of emerging industries (grant number JCYJ20120831165730901) and the National High-Tech Research and Development Plan of China (863) (number 2012AA09A408). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.