Effective texture classification by texton encoding induced statistical features

Jin Xie, Lei Zhang, Jia You, Chi Keung Simon Shiu

Research output: Journal article publicationJournal articleAcademic researchpeer-review

34 Citations (Scopus)

Abstract

Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. Recently, texton learning based texture classification approaches have been widely studied, where the textons are usually learned via K-means clustering or sparse coding methods. However, the K-means clustering is too coarse to characterize the complex feature space of textures, while sparse texton learning/encoding is time-consuming due to the l0-norm or l1-norm minimization. Moreover, these methods mostly compute the texton histogram as the statistical features for classification, which may not be effective enough. This paper presents an effective and efficient texton learning and encoding scheme for texture classification. First, a regularized least square based texton learning method is developed to learn the dictionary of textons class by class. Second, a fast two-step l2-norm texton encoding method is proposed to code the input texture feature over the concatenated dictionary of all classes. Third, two types of histogram features are defined and computed from the texton encoding outputs: coding coefficients and coding residuals. Finally, the two histogram features are combined for classification via a nearest subspace classifier. Experimental results on the CUReT, KTH-TIPS and UIUC datasets demonstrated that the proposed method is very promising, especially when the number of available training samples is limited.
Original languageEnglish
Pages (from-to)447-457
Number of pages11
JournalPattern Recognition
Volume48
Issue number2
DOIs
Publication statusPublished - 1 Jan 2015

Keywords

  • Sparse representation
  • Texton learning
  • Texture classification
  • Texture feature extraction

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

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