A framework for analyzing texture descriptors. Ahonen, T. & Pietikäinen, M. In Proc. Third International Conference on Computer Vision Theory and Applications (VISAPP 2008), volume 1, pages 507-512, 2008. abstract bibtex This paper presents a new unified framework for texture descriptors such as Local Binary Patterns (LBP)
and Maximum Response 8 (MR8) that are based on histograms of local pixel neighborhood properties. This
framework is enabled by a novel filter based approach to the LBP operator which shows that it can be seen
as a special filter based texture operator. Using the proposed framework, the filters to implement LBP are
shown to be both simpler and more descriptive than MR8 or Gabor filters in the texture categorization task. It
is also shown that when the filter responses are quantized for histogram computation, codebook based vector
quantization yields slightly better results than threshold based binning at the cost of higher computational
complexity.
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title = {A framework for analyzing texture descriptors.},
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year = {2008},
pages = {507-512},
volume = {1},
city = {Madeira, Portugal},
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abstract = {This paper presents a new unified framework for texture descriptors such as Local Binary Patterns (LBP)
and Maximum Response 8 (MR8) that are based on histograms of local pixel neighborhood properties. This
framework is enabled by a novel filter based approach to the LBP operator which shows that it can be seen
as a special filter based texture operator. Using the proposed framework, the filters to implement LBP are
shown to be both simpler and more descriptive than MR8 or Gabor filters in the texture categorization task. It
is also shown that when the filter responses are quantized for histogram computation, codebook based vector
quantization yields slightly better results than threshold based binning at the cost of higher computational
complexity.},
bibtype = {inProceedings},
author = {Ahonen, T and Pietikäinen, M},
booktitle = {Proc. Third International Conference on Computer Vision Theory and Applications (VISAPP 2008)}
}
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