Title :
Rotation-invariant local binary pattern texture classification
Author :
Doshi, Niraj P. ; Schaefer, Gerald
Author_Institution :
Dept. of Comput. Sci., Loughborough Univ., Loughborough, UK
Abstract :
Texture analysis and classification is a well researched topic in computer vision. Since textures are captured at arbitrary angles, the derivation of rotation-invariant texture descriptors has received much attention. A group of high performing texture algorithms are based on the concept of local binary patterns (LBP). These algorithms are very efficient as they typically rely solely on local comparison operations and can also be readily extended (and in fact simplified) to be rotation invariant. In this paper, we provide an overview of eleven LBP-based texture algorithms and benchmark them on a set of rotated Brodatz textures.
Keywords :
computer vision; image classification; image texture; LBP-based texture algorithms; arbitrary angles; computer vision; high performing texture algorithms; rotated Brodatz textures; rotation-invariant local binary pattern texture classification; rotation-invariant texture descriptors; texture analysis; Accuracy; Algorithm design and analysis; Benchmark testing; Computer vision; Databases; Histograms; Support vector machines; Texture analysis; evaluation; local binary patterns (LBP); texture classification;
Conference_Titel :
ELMAR, 2012 Proceedings
Conference_Location :
Zadar
Print_ISBN :
978-1-4673-1243-1