An optimized skin texture model using gray-level co-occurrence matrix

Maktabdar Oghaz, Mahdi and Maarof, Mohd Aizaini and Rohani, Mohd Foad and Zainal, Anazida and Shaid, Syed Zainudeen Mohd (2019) An optimized skin texture model using gray-level co-occurrence matrix. Neural Computing and Applications, 31 (6). pp. 1835-1853. ISSN 1433-3058

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Official URL: https://doi.org/10.1007/s00521-017-3164-8

Abstract

Texture analysis is devised to address the weakness of color-based image segmentation models by considering the statistical and spatial relations among the group of neighbor pixels in the image instead of relying on color information of individual pixels solely. Due to decent performance of the gray-level co-occurrence matrix (GLCM) in texture analysis of natural objects, this study employs this technique to analyze the human skin texture characteristics. The main goal of this study is to investigate the impact of major GLCM parameters including quantization level, displacement magnitudes, displacement direction and GLCM features on skin segmentation and classification performance. Each of these parameters has been assessed and optimized using an exhaustive supervised search from a fairly large initial feature space. Three supervised classifiers including Random Forest, Support Vector Machine and Multilayer Perceptron have been employed to evaluate the performance of the feature space subsets. Evaluation results using Edith Cowan University (ECU) dataset showed that the proposed texture-assisted skin detection model outperformed pixelwise skin detection by significant margin. The proposed method generates an F-score of 91.98, which is satisfactory, considering the challenging scenario in ECU dataset. Comparison of the proposed texture-assisted skin detection model with some state-of-the-art skin detection models indicates high accuracy and F-score of the proposed model. The findings of this study can be used in various disciplines, such as face recognition, skin disorder and lesion recognition, and nudity detection.

Item Type: Journal Article
Faculty: ARCHIVED Faculty of Science & Technology (until September 2018)
Depositing User: Lisa Blanshard
Date Deposited: 09 Mar 2020 12:12
Last Modified: 09 Sep 2021 16:08
URI: https://arro.anglia.ac.uk/id/eprint/705265

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