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  • Research Article
  • Open Access

Texture Classification Using Sparse Frame-Based Representations

EURASIP Journal on Advances in Signal Processing20062006:052561

  • Received: 31 August 2004
  • Accepted: 2 June 2005
  • Published:


A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error, given a sparseness constraint, is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration, and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work, yielding excellent overall performance.


  • Test Image
  • Iterative Procedure
  • Texture Classification
  • Representation Error
  • Image Block

Authors’ Affiliations

Department of Electrical and Computer Engineering, University of Stavanger, Stavanger, 4036, Norway


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© Skretting and Husøy 2006