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2021
DOI: 10.1088/1742-6596/1892/1/012018
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A novel hybrid feature extraction method using LTP, TFCM, and GLCM

Abstract: Image classification and feature extraction have been studied extensively and used efficiently in several applications. This paper suggests a novel method by combining three main methods for texture feature extraction. The proposed method is based on Local Ternary Pattern (LTP), Texture Feature Coding Method (TFCM), and Gray Level Cooccurrence Matrix (GLCM). We have entitled our method as GCLTP which is stand for Gray Coding Local Ternary Pattern. The combination of LTP, TFCM, and GLCM is assigned a unique val… Show more

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Cited by 10 publications
(7 citation statements)
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“…Another study [14] combined the GLCM method with six other methods, resulting in the extraction of 1308 features, including 56 from GLCM. The method employed the Principal Component Analysis (PCA) to reduce the number of extracted features and the adaptive histogram equalization algorithm to enhance image contrast.…”
Section: Related Workmentioning
confidence: 99%
See 1 more Smart Citation
“…Another study [14] combined the GLCM method with six other methods, resulting in the extraction of 1308 features, including 56 from GLCM. The method employed the Principal Component Analysis (PCA) to reduce the number of extracted features and the adaptive histogram equalization algorithm to enhance image contrast.…”
Section: Related Workmentioning
confidence: 99%
“…Typically, GLCM is known for its ability to extract robust statistical texture features, with many researchers utilizing it as a secondary feature extraction method, in addition to other techniques, in the MATLAB image processing toolbox, where only four features (contrast, correlation, energy, and inverse difference moment/homogeneity) [9][10][11][12][13] 2). However, GLCM is usually used as a secondary feature extractor method [14,15].…”
Section: Introductionmentioning
confidence: 99%
“…Recently, computer vision, ML, and DL have been used to melanoma detection with great success [7][8][9][10][11]. Using a suggested CNN architecture and benchmark datasets [12], this paper applies deep learning to the problem of detecting malignant melanoma.…”
Section: Introductionmentioning
confidence: 99%
“…Consequently, it is also utilized for logical, performance, and security reasons; it has an additional layer of an edge that supports and resembles those of cloud computing and IoT applications [8]. Furthermore, low corresponding state-of-the-earth resolutions are provided.…”
Section: Introductionmentioning
confidence: 99%