Texture Illumination Invariant Feature Extraction Algorithm Based on BM3D Pre-processing

doi:10.3969/j.issn.1006-2475.2015.10.011

Abstract

Abstract: In order to eliminate the effects of changing illumination on image structure information, a three dimensional block-matching (BM3D) pre-processing-based texture illumination invariant feature extraction algorithm is proposed. With the excellent denoising feature of BM3D algorithm, this method firstly uses BM3D denoising method to denoise each color channel, and uses wavelet transform to get the low and high frequency component of logarithmic domain of each color channel of image, secondly, uses the wavelet denoising method and Bayes-Shrink denoising method to denoise the low and high frequency components respectively, and thirdly constructs illumination invariant, using the principal component analysis (PCA) to reduce the feature dimension, obtains the feature vector, and lastly uses the K-NFL classifier to classify image. Experimental results based on Outex_TC_00014 texture database show that the proposed method delivers good classification performance.

Key words: three dimensional block-matching (BM3D), wavelet transform, Bayes-Shrink, principal component analysis (PCA), K-nearest feature line classifier

CLC Number:

TP391.41

ZHAN Jun-jie, SHANG Zhao-wei. Texture Illumination Invariant Feature Extraction Algorithm Based on BM3D Pre-processing[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2015.10.011.

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