Title :
Completed Local Binary Count for Rotation Invariant Texture Classification
Author :
Yang Zhao ; De-Shuang Huang ; Wei Jia
Author_Institution :
Dept. of Autom., Univ. of Sci. & Technol. of China, Hefei, China
Abstract :
In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.
Keywords :
binary codes; feature extraction; image classification; image texture; local binary count codes; local binary grayscale difference information; local binary structural information; local descriptor; rotation invariant texture classification; visual microstructure; Data mining; Databases; Encoding; Gray-scale; Histograms; Lighting; Training; Local binary count (LBC); local binary pattern (LBP); rotation invariance; texture classification;
Journal_Title :
Image Processing, IEEE Transactions on
DOI :
10.1109/TIP.2012.2204271