Authors:
Yu Zhang
1
;
Stéphane Bres
2
and
Liming Chen
1
Affiliations:
1
Universite de Lyon, France
;
2
LIRIS-INSA de Lyon, France
Keyword(s):
Local Binary Pattern, Feature Extraction, Object Recognition, Patch Reorganization.
Related
Ontology
Subjects/Areas/Topics:
Color and Texture Analyses
;
Computer Vision, Visualization and Computer Graphics
;
Features Extraction
;
Image and Video Analysis
Abstract:
In this paper, we propose a novel representation, called sampled multi-scale color Local Binary Pattern (SMCLBP),
and apply it to Visual Object Classes (VOC) Recognition. The Local Binary Pattern (LBP) has been
proven to be effective for image representation, but it is too local to be robust. Meanwhile such a design cannot
fully exploit the discriminative capacity of the features available and deal with various changes in lighting and
viewing conditions in real-world scenes. In order to address these problems, we propose SMC-LBP, which
randomly samples the neighboring pixels across different scale circles, instead of pixels from individual circular
in the original LBP scheme. The proposed descriptor presents several advantages: (1) It encodes not only
single scale but also multiple scales of image patterns, and hence provides a more complete image information
than the original LBP descriptor; (2) It cooperates with color information, therefore its photometric invariance
property and
discriminative power is enhanced. The experimental results on the PASCAL VOC 2007
image benchmark show significant accuracy improvement by the proposed descriptor compared with both the
original LBP and other popular texture descriptors.
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