Colour in Context
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Computer Vision Center

Texton theory revisited: a bag-of-words approach to combine textons

Pattern Recognition (PR), Volume 45, Number 12, page 4312--4325 - december 2012
IF: 2.632. area: COMPUTER SCI., ARTIFICIAL INT.. Quartile: Q1.
 
Download the publication : PR2011-19.pdf [2Mo]  
The aim of this paper is to revisit an old theory of texture perception and update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages. We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these low-dimensional texton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets.

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BibTex references

@Article\{AlV2012,
  author       = "Susana Alvarez Fernandez and Maria Vanrell",
  title        = "Texton theory revisited: a bag-of-words approach to combine textons",
  journal      = "Pattern Recognition (PR)",
  number       = "12",
  volume       = "45",
  pages        = "4312--4325",
  month        = "december",
  year         = "2012",
  keywords     = "colour-texture attributes, perceptual descriptor, colour textons",
  abstract     = "The aim of this paper is to revisit an old theory of texture perception and update its computational implementation by extending it to colour. With this in mind we try to capture the optimality of perceptual systems. This is achieved in the proposed approach by sharing well-known early stages of the visual processes and extracting low-dimensional features that perfectly encode adequate properties for a large variety of textures without needing further learning stages.

We propose several descriptors in a bag-of-words framework that are derived from different quantisation models on to the feature spaces. Our perceptual features are directly given by the shape and colour attributes of image blobs, which are the textons. In this way we avoid learning visual words and directly build the vocabularies on these low-dimensional texton spaces. Main differences between proposed descriptors rely on how co-occurrence of blob attributes is represented in the vocabularies. Our approach overcomes current state-of-art in colour texture description which is proved in several experiments on large texture datasets.",
  keywords     = "colour-texture attributes, perceptual descriptor, colour textons",
  ifactor      = "2.632",
  quartile     = "Q1",
  area         = "COMPUTER SCI., ARTIFICIAL INT.",
  url          = "http://cat.cvc.uab.es/Public/Publications/2012/AlV2012"
}

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