Local Entropy Pattern for Feature Extraction of Texture Images

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Shakoor Mohammad Hossein; Tajeripour Farshad

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Journal Paper

Paper Information

Journal: MACHINE VISION AND IMAGE PROCESSING Year:2017 | Volume:3 | Issue:2 Page(s): 73-85

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Information Journal Paper

Title

Local Entropy Pattern for Feature Extraction of Texture Images

Author(s)

Shakoor Mohammad Hossein | Tajeripour Farshad | Issue Writer Certificate

Keywords

Texture Classification

Feature Extraction

Local Binary Patterns

Local Variance

Local Entropy Pattern

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.

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APA: Copy

Shakoor, Mohammad Hossein, & Tajeripour, Farshad. (2017). Local Entropy Pattern for Feature Extraction of Texture Images. MACHINE VISION AND IMAGE PROCESSING, 3(2 ), 73-85. SID. https://sid.ir/paper/265732/en

Vancouver: Copy

Shakoor Mohammad Hossein, Tajeripour Farshad. Local Entropy Pattern for Feature Extraction of Texture Images. MACHINE VISION AND IMAGE PROCESSING[Internet]. 2017;3(2 ):73-85. Available from: https://sid.ir/paper/265732/en

IEEE: Copy

Mohammad Hossein Shakoor, and Farshad Tajeripour, “Local Entropy Pattern for Feature Extraction of Texture Images,” MACHINE VISION AND IMAGE PROCESSING, vol. 3, no. 2 , pp. 73–85, 2017, [Online]. Available: https://sid.ir/paper/265732/en

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