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A novel hybrid feature extraction method using LTP, TFCM, and GLCM

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 value used to extract the features of an image. GCLTP is tested using images are taken from the Brodatz database. A set of 22 features were extracted from images. GCLTP is experimentally accomplished a high accuracy in classification by using the most known classifiers.


Introduction
The classification of images is used in many areas, for example, the medical classification of the disease. It is also utilized in the classification of texture images. There are many ordinary methods in this area which depend on the extraction of texture information, like Gray Level Cooccurrence Matrix (GLCM) [1,2], Texture Spectrum [3], Local Binary Pattern (LBP) [4], Local Ternary Pattern (LTP) [5], and Texture Feature Coding Method (TFCM) [6]. The features extraction depends on the local texture properties for each pixel of the images, thus obtaining many statistics. However, the researchers have developed several hybridization methods to reach more superior results for feature extraction and classification. In the literature, the combination of LTP, TFCM, and GLCM is never done before. Therefore, this paper proposes a novel method by combining three principal methods for texture feature extraction.
The rest of the paper is divided into five sections. Firstly, the related works are discussed in section two. Secondly, section three states the explanation of the methodology of the proposed method. Next, the performance results are illustrated in section four. Finally, section five concludes the whole study.

Methodology
Since our proposed method focuses on feature extraction based on LTP, TFCM, and GLCM, it will be known as GCLTP for Gray Coding Local Ternary Pattern. The mentioned methods which are applied individually and in the hybrid mode on an image, and a set of 22 features (Inverse difference (INV), Inverse difference normalized (INN), Inverse difference moment normalized, Homogeneity, Contrast, Autocorrelation, Correlation, Cluster Prominence, Cluster Shade, Dissimilarity, Energy, Entropy, Maximum probability, Sum of Squares, Variance, Sum average, Sum variance, Sum entropy, Difference variance, Difference entropy, Information measure of correlation1, and Information measure of correlation2) are extracted. Then, the extracted features -for each method -will be combined into one dataset. GCLTP is described as follows:

Feature Extraction
In the beginning, each image in the dataset is divided into 4 sub-images. Then, we utilize LTP applied to the first, second, third, and the last sub-image. Consequently, we get three matrices (LTPU, LTPN, and LTPL). The emerged matrices are used to extract the features set. Also, these resulting matrices will 3 be inputted to the second method TFCM. Afterwards, TFCM will export three new matrices (TFCMU, TFCMN, and TFCML), and the feature will be extracted from them.
After that, the last resulting matrices are considered as an input to the third method GLCM through which the features are also extracted. GLCM produces five matrices for each of the previous outputs (GLCMA, GLCM1, GLCM2, GLCM3, and GLCM4). Moreover, the features are extracted from the five matrices.

Classification
In this section, each dataset is divided into a training and testing set. Training set with 80% of the original dataset, while the testing set is 20%. As it was previously mentioned, the support vector machine is used with a linear kernel and decision tree classifiers to evaluate our GCLTP. In the next section, the discussion of the experimental results will be shown-the calculation of the precision, recall, f1-score, and accuracy for each dataset. Precision or sensitivity is a measurement that counts the number of accurate positive predictions made [18]. It can be calculated using the following formula: Recall is a measurement that counts the number of accurate positive predictions made out of all positive predictions that might have been made [18]. It can be calculated using the following formula: F1-Score or F-Measure is a combination of precision and recall in one measure formula [18]. It can be calculated by: Finally, accuracy [18] can be calculated based on the confusion matrix by:

Experimental Results
In this study, we have conducted two classifiers (L-SVM and DT) to verify the efficiency of GCLTP. We have used a part of a well-known dataset, so-called Brodatz texture images. This part consists of thirteen images with seven different rotation angles: 0, 30, 60, 90, 120, 150, and 200 degrees. All images are 512x512 pixels [18]. The GCLTP is implemented using MATLAB R2018a, while classification algorithms are established using Python v3 with Spyder under anaconda. All experiments are run on a machine with Intel Core i7-6500U CPU of 2.50 GHz and 8.0 GB RAM. Figure 2 show the main thirteen images in the dataset.

Figure 2.
The main thirteen images in the Brodatz texture dataset [19] In this subsection, we will introduce our method results of testing the datasets' performance that are produced from the GCLTP system. We use SVM with the linear kernel and DT classifiers. To account for the imbalance data label, we have considered the precision, recall and F1 score. Accuracy as the common performance measuring is calculated for each benchmark. Table 1 and Table 2 illustrate the datasets of classification results. Consequently, we further summarize F1-score for GCLTPL, GCLTPN, and GCLTPU datasets in Figure 3, Figure 4, and Figure 5, respectively.  Figure 3 and Figure 5 conclude that F1-score accuracy is remarkably high for all GCLTPL and GCLTPU benchmarks with no less of 0.801, while the GCLTPN (Figure 4) has the highest F1-score accuracies in both SVM-L and DT classifiers.

Conclusions
In this paper, we have proposed a novel texture feature extraction method. It has aimed to extract feature from the image. We combined three main feature extraction methods known as (LTP, TFCM, and GLCM). The proposed method is named as GCLTP. To evaluate the performance of GCLTP, first, we carried out experiments on Brodatz dataset. Second, two classifiers are used to evaluate the performance of our method. The results have shown that GCLTP performance is at least as accurate as other wellestablished approaches in the literature. The main interesting future research is applying the GCLTP to colored image and enhancing the feature extraction's time.