Impact of topology-related attributes from Local Binary Patterns on texture classificationReport as inadecuate

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1 ADAGIO - Applying Discrete Algorithms to Genomics and Imagery LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications 2 ENSTA ParisTech UEI - Unité d-Électronique et d-informatique 3 PRIP - Pattern Recognition and Image Processing Group

Abstract : A general texture description model is proposed, using topol-ogy related attributes calculated from Local Binary Patterns LBP.
The proposed framework extends and generalises existing LBP-based descrip-tors like LBP-rotation invariant uniform patterns LBP riu2, and Local Binary Count LBC.
Like them, it allows contrast and rotation invari-ant image description using more compact descriptors than classic LBP.
However, its expressiveness, and then its discrimination capability, is higher, since it includes additional information, including the number of connected components.
The impact of the different attributes on texture classification performance is assessed through a systematic comparative evaluation, performed on three texture datasets.
The results validate the interest of the proposed approach, by showing that some combinations of attributes outperform state-of-the-art LBP-based texture descriptors.

Keywords : texture classification local binary pattern local descriptor

Author: Thanh Phuong Nguyen - Antoine Manzanera - Walter G.
Kropatsch -



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