1
School of Computer Engineering, Shiraz, Islamic Azad University
2
School of Electrical and Computer Engineering, Shiraz University
Abstract
There are many methods for feature extraction from texture images. Local Binary Pattern (LBP) is one of the most important of these methods. It is a simple method for implementation and can extracttexture features efficiently.LBP can be combined with local variance (VAR) to provide higher classification rate. In this paper, a new method is proposedwhich is named Local Entropy Pattern (LEP). The equation of this method is similar to Entropy literally, butit is differ from Entropy in some issues. The proposed method is more robust to noise than LBP and VAR. In addition, by combiningit's features with LBP features the classification rate increases significantly and it provides higher accuracy than LBP/VAR. Local Entropy Pattern shows dissimilarity of a local neighborhood. This approach has all positive points of LBP and some state-of-art similar methods. It is not only rotation and grayscale invariant but also noise robust.
Shakoor, M. H., & Tajeripour, F. (2016). Local Entropy Pattern for Feature Extraction of Texture Images. Journal of Machine Vision and Image Processing, 3(2), 73-85.
MLA
Mohammad Hossein Shakoor; Farshad Tajeripour. "Local Entropy Pattern for Feature Extraction of Texture Images". Journal of Machine Vision and Image Processing, 3, 2, 2016, 73-85.
HARVARD
Shakoor, M. H., Tajeripour, F. (2016). 'Local Entropy Pattern for Feature Extraction of Texture Images', Journal of Machine Vision and Image Processing, 3(2), pp. 73-85.
VANCOUVER
Shakoor, M. H., Tajeripour, F. Local Entropy Pattern for Feature Extraction of Texture Images. Journal of Machine Vision and Image Processing, 2016; 3(2): 73-85.
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