TY - GEN
T1 - Impact of topology-related attributes from local binary patterns on texture classification
AU - Nguyen, Thanh Phuong
AU - Manzanera, Antoine
AU - Kropatsch, Walter G.
N1 - Publisher Copyright:
© Springer International Publishing Switzerland 2015.
PY - 2015/1/1
Y1 - 2015/1/1
N2 - 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 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.
AB - 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 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.
KW - Local binary pattern
KW - Local descriptor
KW - Texture classification
U2 - 10.1007/978-3-319-16181-5_6
DO - 10.1007/978-3-319-16181-5_6
M3 - Conference contribution
AN - SCOPUS:84928805636
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 80
EP - 93
BT - Computer Vision - ECCV 2014 Workshops, Proceedings
A2 - Rother, Carsten
A2 - Bronstein, Michael M.
A2 - Agapito, Lourdes
PB - Springer Verlag
T2 - 13th European Conference on Computer Vision, ECCV 2014
Y2 - 6 September 2014 through 12 September 2014
ER -