Contributions to Proceedings:
T. Nguyen, A. Manzanera, W. Kropatsch:
"Impact of topology-related attributes from local binary patterns on texture classification";
in: "Proceedings of the 2nd Intl. Workshop on Computer Vision With Local Binary Pattern Variants (ECCV'14)",
A. Hadid, J. Dugelay, S. Li (ed.);
issued by: Abdenour Hadid, Jean-Luc Dugelay, and Stan Z. Li;
Proceedings of the 2nd Intl. Workshop on Computer Vision With Local Binary Pattern Variants (ECCV'14),
Zurich, CH, September 2014,
A general texture description model is proposed, using topology
related attributes calculated from Local Binary Patterns (LBP). The
proposed framework extends and generalises existing LBP-based descriptors
like LBP-rotation invariant uniform patterns (LBPriu2), and Local
Binary Count (LBC). Like them, it allows contrast and rotation invariant
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 di erent attributes on texture
classi cation 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.
local binary pattern, local descriptor, texture classi cation
Created from the Publication Database of the Vienna University of Technology.