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A comprehensive study of texture analysis based on local binary patterns
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Paper Abstract

One of the main goals of texture analysis is to provide a robust mathematical description of the spatial behavior of intensity values in any given neighborhood. These local distributions {called textures{ characterize object surfaces and are used for pattern identication and recognition of images. However, some spatial patterns may vary from quite simple stripes to randomness, where textures look like unstructured noise. Since textures can exhibit a large number of properties such as surface materials and geometry of the lighting sources, many dierent approaches have been proposed. A featured method is the modication of Wang's algorithm made by Ojala et al, the so-called local binary patterns (LBP). The LBP algorithm uses a 3×3 square mask named "texture spectrum" which represents a neighborhood around a central pixel. The values in the square mask are compared with the central pixel and then multiplied by a weighting function according with their positions. This technique has become popular due to its computational simplicity and more importantly for encoding a powerful signature for describing textures. Specially, it has gained increased importance in image classication, where the success not only depends on a robust classier but also relies in a good selection of the feature descriptors. However, Ojala's algorithm presents some limitations such as noise sensitivity and lack of invariance to rotational changes. This fact has fostered many extensions of the original LBP approach that in many cases are based on minor changes in order to attain e.g. illumination and rotational invariance or improving the robustness to noise. In this paper we present a detailed overview of the LBP algorithm and other recently modications. In addition, we perform a texture classication study with seven algorithms in presence of rotational changes, noise degradation, contrast information, and dierent sizes of LBP masks using the USC-SIPI database. The LBP histograms have been evaluated using the Kullback-Leibler distance. This study will be a valuable insight for establishing a robust and ecient texture descriptor to solve real world problems.

Paper Details

Date Published: 30 April 2012
PDF: 12 pages
Proc. SPIE 8436, Optics, Photonics, and Digital Technologies for Multimedia Applications II, 84360E (30 April 2012); doi: 10.1117/12.923558
Show Author Affiliations
Rodrigo Nava, Univ. Nacional Autónoma de México (Mexico)
Gabriel Cristóbal, Instituto de Óptica (Spain)
Boris Escalante-Ramírez, Univ. Nacional Autónoma de México (Mexico)

Published in SPIE Proceedings Vol. 8436:
Optics, Photonics, and Digital Technologies for Multimedia Applications II
Peter Schelkens; Touradj Ebrahimi; Gabriel Cristóbal; Frédéric Truchetet; Pasi Saarikko, Editor(s)

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