Texture Features For Classification Research Articles

Enhancing texture feature for mineral classification in tight sandstone rock thin-section images using super-resolution techniques

In petrological studies, accurately classifying minerals and pore objects in rock thin-section images is essential for understanding geological conditions of oil and gas reservoirs. A key challenge in current methodologies is the limited image resolution, which restricts the accuracy of texture feature recognition crucial for effective classification. This study introduces super-resolution (SR) techniques to overcome this limitation, aiming to enhance image resolution and thereby improve the recognition and classification of texture features in thin-section imagery. To evaluate the effectiveness of various SR methods, we conducted a series of experiments evaluating their impact on mineral type classification, employing advanced techniques such as Gabor filtering and SVM classifiers for texture feature extraction and classification.Our experiments demonstrated that all evaluated SR techniques can significantly enhance classification accuracy, with each method improving accuracy by at least 19.3% for mixed color spaces. Notably, the Enhanced Deep Super-Resolution (EDSR) model, while less intuitive for human visual assessment, emerged as highly effective in extracting intricate texture features, resulting in an approximate 24.2% increase in accuracy within multi color spaces. These results suggest that the most effective SR models for enhancing texture details are not necessarily aligned with human visual evaluation. The novelty of this work is grounded in its comprehensive assessment of SR techniques in petrological imaging, marking a substantial advancement over previous methods. This study not only enhances the discernibility of critical texture features in rock thin-section imagery but also provides a clear pathway for integrating advanced image processing techniques into petrological studies.

A COPRAS-based Approach to Multi-Label Feature Selection for Text Classification

In this article, we present a novel approach for text classification feature selection using a pseudo-relation matrix constructed with ridge regression. This approach involves generating a matrix that captures the relationship between features and target variables and then using that matrix to create a multi-criteria decision making (MCDM) problem. We then utilize the Complex Proportional Assessment (COPRAS) method to rank features in order of importance. To assess the performance of our proposed algorithm, we conduct tests on ten real-world text datasets and compare our results to the existing methods. Our findings indicate that the proposed approach outperforms other methods, and we provide statistical significance to support our claims. Our proposed algorithm provides a viable answer for researchers looking for efficient and effective feature selection methods for text data analysis, which can lead to a better understanding and interpretation of the underlying text.

Application of Machine Learning to Tree Species Classification Using Active and Passive Remote Sensing: A Case Study of the Duraer Forestry Zone

The technology of remote sensing-assisted tree species classification is increasingly developing, but the rapid refinement of tree species classification on a large scale is still challenging. As one of the treasures of ecological resources in China, Arxan has 80% forest cover, and tree species classification surveys guarantee ecological environment management and sustainable development. In this study, we identified tree species in three samples within the Arxan Duraer Forestry Zone based on the spectral, textural, and topographic features of unmanned aerial vehicle (UAV) multispectral remote sensing imagery and light detection and ranging (LiDAR) point cloud data as classification variables to distinguish among birch, larch, and nonforest areas. The best extracted classification variables were combined to compare the accuracy of the random forest (RF), support vector machine (SVM), and classification and regression tree (CART) methodologies for classifying species into three sample strips in the Arxan Duraer Forestry Zone. Furthermore, the effect on the overall classification results of adding a canopy height model (CHM) was investigated based on spectral and texture feature classification combined with field measurement data to improve the accuracy. The results showed that the overall accuracy of the RF was 79%, and the kappa coefficient was 0.63. After adding the CHM extracted from the point cloud data, the overall accuracy was improved by 7%, and the kappa coefficient increased to 0.75. The overall accuracy of the CART model was 78%, and the kappa coefficient was 0.63; the overall accuracy of the SVM was 81%, and the kappa coefficient was 0.67; and the overall accuracy of the RF was 86%, and the kappa coefficient was 0.75. To verify whether the above results can be applied to a large area, Google Earth Engine was used to write code to extract the features required for classification from Sentinel-2 multispectral and radar topographic data (create equivalent conditions), and six tree species and one nonforest in the study area were classified using RF, with an overall accuracy of 0.98, and a kappa coefficient of 0.97. In this paper, we mainly integrate active and passive remote sensing data for forest surveying and add vertical data to a two-dimensional image to form a three-dimensional scene. The main goal of the research is not only to find schemes to improve the accuracy of tree species classification, but also to apply the results to large-scale areas. This is necessary to improve the time-consuming and labor-intensive traditional forest survey methods and to ensure the accuracy and reliability of survey data.

Improving Automatic Melanoma Diagnosis Using Deep Learning-Based Segmentation of Irregular Networks

Deep learning has achieved significant success in malignant melanoma diagnosis. These diagnostic models are undergoing a transition into clinical use. However, with melanoma diagnostic accuracy in the range of ninety percent, a significant minority of melanomas are missed by deep learning. Many of the melanomas missed have irregular pigment networks visible using dermoscopy. This research presents an annotated irregular network database and develops a classification pipeline that fuses deep learning image-level results with conventional hand-crafted features from irregular pigment networks. We identified and annotated 487 unique dermoscopic melanoma lesions from images in the ISIC 2019 dermoscopic dataset to create a ground-truth irregular pigment network dataset. We trained multiple transfer learned segmentation models to detect irregular networks in this training set. A separate, mutually exclusive subset of the International Skin Imaging Collaboration (ISIC) 2019 dataset with 500 melanomas and 500 benign lesions was used for training and testing deep learning models for the binary classification of melanoma versus benign. The best segmentation model, U-Net++, generated irregular network masks on the 1000-image dataset. Other classical color, texture, and shape features were calculated for the irregular network areas. We achieved an increase in the recall of melanoma versus benign of 11% and in accuracy of 2% over DL-only models using conventional classifiers in a sequential pipeline based on the cascade generalization framework, with the highest increase in recall accompanying the use of the random forest algorithm. The proposed approach facilitates leveraging the strengths of both deep learning and conventional image processing techniques to improve the accuracy of melanoma diagnosis. Further research combining deep learning with conventional image processing on automatically detected dermoscopic features is warranted.

Detection of early decayed oranges by structured-illumination reflectance imaging coupling with texture feature classification models.

Citrus fruits are susceptible to fungal infection after harvest. To reduce the economic loss, it is necessary to reject the infected citrus fruit before storage and transportation. However, the infected area in the early stage of decay is almost invisible on the fruit surface, so the detection of early decayed citrus is very challenging. In this study, a structured-illumination reflectance imaging (SIRI) system combined with a visible light-emitting diode (LED) lamp and a monochrome camera was developed to detect early fungal infection in oranges. Under sinusoidal modulation illumination with spatial frequencies of 0.05, 0.15, and 0.25 cycles mm–1, three-phase-shifted images with phase offsets of − 2π/3, 0, and 2π/3 were acquired for each spatial frequency. The direct component (DC) and alternating component (AC) images were then recovered by image demodulation using a three-phase-shifting approach. Compared with the DC image, the decayed area can be clearly identified in the AC image and RT image (AC/DC). The optimal spatial frequency was determined by analyzing the AC image and pixel intensity distribution. Based on the texture features extracted from DC, AC, and RT images, four kinds of classification models including partial least square discriminant analysis (PLS-DA), support vector machine (SVM), least squares-support vector machine (LS-SVM), and k-nearest neighbor (KNN) were established to detect the infected oranges, respectively. Model optimization was also performed by extracting important texture features. Compared to all models, the PLS-DA model developed based on eight texture features of RT images achieved the optimal classification accuracy of 96.4%. This study showed for the first time that the proposed SIRI system combined with appropriate texture features and classification model can realize the early detection of decayed oranges.

A novel feature selection technique for enhancing performance of unbalanced text classification problem

Since the last few decades, Text Classification (TC) is being witnessed as an important research direction due to the availability of a huge amount of digital text documents on the web. It would be tedious to manually organize and label them by human experts. Again digging a large number of highly sparse terms and skewed categories present in the documents put a lot of challenges in the correct labeling of the unlabeled documents. Hence feature selection is an essential aspect in text classification, which aims to select more concise and relevant features for further mining of the documents. Additionally, if the text in the document set is associated with multiple categories and the distribution of classes in the dataset is unbalanced, it imposes more challenges on the suitable selection of features for text classification. In this paper, a Modified Chi-Square (ModCHI) based feature selection technique is proposed for enhancing the performance of classification of multi-labeled text documents with unbalanced class distributions. It is an improved version of the Chi-square (Chi) method, which emphasizes selecting maximum features from the classes with a large number of training and testing documents. Unlike Chi, in which the top features are selected with top Chi value, in this proposed technique a score is calculated by considering the total number of relevant documents corresponding to each class with respect to the total number of documents in the original dataset. According to the score the features related to the highly relevant classes as well as high Chi-square value are selected for further processing. The proposed technique is verified with four different classifiers such as Linear SVM (LSVM), Decision tree (DT), Multilevel KNN (MLKNN), Random Forest (RF) over Reuters benchmark multi-labeled, multi-class, unbalanced dataset. The effectiveness of the model is also tested by comparing it with four other traditional feature selection techniques such as term frequency-inverse document frequency (TF-IDF), Chi-square, and Mutual Information (MI). From the experimental outcomes, it is clearly inferred that LSVM with ModCHI produces the highest precision value of 0.94, recall value of 0.80, f-measure of 0.86 and the least hamming loss value of 0.003 with a feature size 1000. The proposed feature selection technique with LSVM produces an improvement of 3.33%, 2.19%, 16.25% in the average precision value, 3.03%, 33.33%, 21.42% in the average recall value, 4%, 34.48%, 14.70% in average F-measure value and 14%, 37.68%, 31.74% in average hamming loss value compared to TF-IDF, Chi and MI techniques respectively. These findings clearly interpret the better performance of the proposed feature selection technique compared to TF_IDF, Chi and MI techniques on the unbalanced Reuters Dataset.

Deep Convolutional Neural Network Based Analysis of Liver Tissues Using Computed Tomography Images

Liver disease is one of the most prominent causes of the increase in the death rate worldwide. These death rates can be reduced by early liver diagnosis. Computed tomography (CT) is a method for the analysis of liver images in clinical practice. To analyze a large number of liver images, radiologists face problems that sometimes lead to the wrong classifications of liver diseases, eventually resulting in severe conditions, such as liver cancer. Thus, a machine-learning-based method is needed to classify such problems based on their texture features. This paper suggests two different kinds of algorithms to address this challenging task of liver disease classification. Our first method, which is based on conventional machine learning, uses texture features for classification. This method uses conventional machine learning through automated texture analysis and supervised machine learning methods. For this purpose, 3000 clinically verified CT image samples were obtained from 71 patients. Appropriate image classes belonging to the same disease were trained to confirm the abnormalities in liver tissues by using supervised learning methods. Our proposed method correctly quantified asymmetric patterns in CT images using machine learning. We evaluated the effectiveness of the feature vector with the K Nearest Neighbor (KNN), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) classifiers. The second algorithm proposes a semantic segmentation model for liver disease identification. Our model is based on semantic image segmentation (SIS) using a convolutional neural network (CNN). The model encodes high-density maps through a specific guided attention method. The trained model classifies CT images into five different categories of various diseases. The compelling results obtained confirm the effectiveness of the proposed model. The study concludes that abnormalities in the human liver could be discriminated and diagnosed by texture analysis techniques, which may also assist radiologists and medical physicists in predicting the severity and proliferation of abnormalities in liver diseases.

Spatial texture feature classification algorithm for high resolution 3D images

Spatial texture feature classification algorithm for high resolution 3D images

Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response

PurposeThis study used machine learning classification of texture features from MRI of breast tumor and peri-tumor at multiple treatment time points in conjunction with molecular subtypes to predict eventual pathological complete response (PCR) to neoadjuvant chemotherapy.Materials and methodThis study employed a subset of patients (N = 166) with PCR data from the I-SPY-1 TRIAL (2002–2006). This cohort consisted of patients with stage 2 or 3 breast cancer that underwent anthracycline–cyclophosphamide and taxane treatment. Magnetic resonance imaging (MRI) was acquired pre-neoadjuvant chemotherapy, early, and mid-treatment. Texture features were extracted from post-contrast-enhanced MRI, pre- and post-contrast subtraction images, and with morphological dilation to include peri-tumoral tissue. Molecular subtypes and Ki67 were also included in the prediction model. Performance of classification models used the receiver operating characteristics curve analysis including area under the curve (AUC). Statistical analysis was done using unpaired two-tailed t-tests.ResultsMolecular subtypes alone yielded moderate prediction performance of PCR (AUC = 0.82, p = 0.07). Pre-, early, and mid-treatment data alone yielded moderate performance (AUC = 0.88, 0.72, and 0.78, p = 0.03, 0.13, 0.44, respectively). The combined pre- and early treatment data markedly improved performance (AUC = 0.96, p = 0.0003). Addition of molecular subtypes improved performance slightly for individual time points but substantially for the combined pre- and early treatment (AUC = 0.98, p = 0.0003). The optimal morphological dilation was 3–5 pixels. Subtraction of post- and pre-contrast MRI further improved performance (AUC = 0.98, p = 0.00003). Finally, among the machine-learning algorithms evaluated, the RUSBoosted Tree machine-learning method yielded the highest performance.ConclusionAI-classification of texture features from MRI of breast tumor at multiple treatment time points accurately predicts eventual PCR. Longitudinal changes in texture features and peri-tumoral features further improve PCR prediction performance. Accurate assessment of treatment efficacy early on could minimize unnecessary toxic chemotherapy and enable mid-treatment modification for patients to achieve better clinical outcomes.

Spatial texture feature classification algorithm for high resolution 3D images

Spatial texture feature classification algorithm for high resolution 3D images

Machine-learning classification of texture features of portable chest X-ray accurately classifies COVID-19 lung infection

BackgroundThe large volume and suboptimal image quality of portable chest X-rays (CXRs) as a result of the COVID-19 pandemic could post significant challenges for radiologists and frontline physicians. Deep-learning artificial intelligent (AI) methods have the potential to help improve diagnostic efficiency and accuracy for reading portable CXRs.PurposeThe study aimed at developing an AI imaging analysis tool to classify COVID-19 lung infection based on portable CXRs.Materials and methodsPublic datasets of COVID-19 (N = 130), bacterial pneumonia (N = 145), non-COVID-19 viral pneumonia (N = 145), and normal (N = 138) CXRs were analyzed. Texture and morphological features were extracted. Five supervised machine-learning AI algorithms were used to classify COVID-19 from other conditions. Two-class and multi-class classification were performed. Statistical analysis was done using unpaired two-tailed t tests with unequal variance between groups. Performance of classification models used the receiver-operating characteristic (ROC) curve analysis.ResultsFor the two-class classification, the accuracy, sensitivity and specificity were, respectively, 100%, 100%, and 100% for COVID-19 vs normal; 96.34%, 95.35% and 97.44% for COVID-19 vs bacterial pneumonia; and 97.56%, 97.44% and 97.67% for COVID-19 vs non-COVID-19 viral pneumonia. For the multi-class classification, the combined accuracy and AUC were 79.52% and 0.87, respectively.ConclusionAI classification of texture and morphological features of portable CXRs accurately distinguishes COVID-19 lung infection in patients in multi-class datasets. Deep-learning methods have the potential to improve diagnostic efficiency and accuracy for portable CXRs.

Radial textures: a new algorithm to analyze meat quality on MRI

Magnetic Resonance Imaging (MRI) was introduced as an alternative to destructive methods for determining the quality parameters of meat products, since MRI is a non-destructive, non-ionizing, and innocuous technique. The use of MRI is linked to the use of computer vision algorithms and data mining techniques. Thus, the success of MRI could not be understood without referring to the implementation of powerful algorithms to handle all data generated. In this study, a new texture algorithm was developed in order to obtain new features based on the radial distribution of the textures from MRI. This new algorithm is called Radial Texture Algorithm (RTA) and it was compared with four classical texture features approaches. The results obtained allow describing the semantic context of the texture features for this new algorithm and the relationship among the features from this algorithm and the classical approaches. Besides, the results obtained by means of these computer vision algorithms were correlated with the results obtained by means of physico-chemical and sensory parameters of the Iberian loins. All of computer vision algorithms achieved correlation coefficients higher than 0.500, noting that RTA reached the highest correlation coefficients in almost all cases. These high correlation coefficients confirm the proposed algorithm as an alternative to the other computational texture features approaches in order to compute the physico-chemical and sensory parameters of meat products in an accurate, fast, non-destructive and efficient way.

Stand species identification using GF-2 imagery and airborne LiDAR data based on SVM classifier

Stand species recognition is one of the key topics in forest research. The new GaoFen-2 (GF-2) satellite imagery contains detailed spectral and texture features. LiDAR data can provide abundant forest structure information. Using deep learning algorithms to identify forest types from optical and LiDAR data can improve recognition accuracy effectively. In this paper, the GaoFeng forest farm of Guangxi Zhuang Autonomous Region is taken as the experimental area, the mean and variance of GF-2 red-green-blue triple band spectra are taken as the spectral characteristics, and the corner two matrix, entropy, contrast and correlation in the gray level co-occurrence matrix are taken as the texture characteristics, a canopy height model (CHM) derived from LiDAR data is built as the structural characteristics, comparing the classification accuracy of using spectral feature, texture feature and structure information or spectral feature, texture feature based on SVM deep learning algorithm to classify in different resolution and different classification levels. The results show that the accuracy of the specific tree species classification by SVM classifier using 2 m-resolution fused GF-2 image combined with spectral, texture and structure features is high, the total classification accuracy is 95.65%, and Kappa coefficient reaches 0.93. As a contrast, the results of 4m-resolution fused GF-2 image using spectral and texture feature classification are poor. Combining the spectral and texture information of forest and the structural information reflected by LiDAR point cloud data can achieve significantly better classification results than each single feature. Compared with several traditional supervised classifiers, the new method needs less specialization and is more beneficial to the large-scale promotion of forestry classification.

Siamese capsule networks with global and local features for text classification

Text classification is a popular research topic in the field of natural language processing and provides wide applications. The existing text classification methods based on deep neural networks can completely extract the local features of text. The text classification models constructed based on these methods yield good experimental results. However, these methods generally ignore the global semantic information of different categories of text and global spatial distance between categories. To some extent, this adversely affects the accuracy of classification. In this study, to address this problem, Siamese capsule networks with global and local features were proposed. A Siamese network was used to glean information about the global semantic differences between categories, which could more accurately represent the semantic distance between different categories. A global memory mechanism was established to store global semantic features, which were then incorporated into the text classification model. Capsule vectors were used to obtain the spatial position relationships of local features, thereby improving the representation capabilities of the features. The experimental results showed that the proposed model achieved better results and performed significantly better on six different public datasets, as compared with ten baseline algorithms.

Stacked Residual Recurrent Neural Networks With Cross-Layer Attention for Text Classification

Text classification is a fundamental task in natural language processing and is essential for many tasks like sentiment analysis and question classification etc. As we all know, different NLP tasks require different linguistic features. Tasks such as text classification requires more semantic features than other tasks such as dependency parsing requiring more syntactic features. Most existing methods focus on improving performance by mixing and calibrating features, without distinguishing the types of features and corresponding effects. In this paper, we propose a stacked residual recurrent neural networks with cross-layer attention model to filter more semantic features for text classification, which named SRCLA. Firstly, we build a stacked network structure to filter different types of linguistic features, and then propose a novel cross-layer attention mechanism that exploits higher-level features to supervise the lower-level features to refine the filtering process. Based on this, more semantic features can be selected for text classification. We conduct experiments on eight text classification tasks, including sentiment analysis, question classification and subjectivity classification and compare with a broad range of baselines. Experimental results show that the proposed approaches achieve the state-of-the-art results on 5 out of 8 tasks.

A New Illumination-Rotation-Invariance Texture Feature Based on Quasi-Periodic Signal Analysis

Texture classification is a classic problem in pattern recognition. It is an effective strategy for improving texture classification to find the texture features with both powerful discrimination and various invariant properties. In this paper, we provide a new insight into texture images, that is, texture images can be treated as quasi-periodic signals. Some new concepts such as Dominant Period Component (DPC), periodic degree (PD), and Main Frequency (MF) are proposed to characterize the properties of quasi-periodic signals. DPC controls the oscillation rate of a quasi-periodic signal and plays a key role in controlling the behavior of the whole signal. So it can serve as a key feature for texture classification. Based on this idea, we propose a new method to extract texture features. The proposed features have both powerful classification ability and rotation-illumination- invariance as well as robustness to noise. Experimental results on three texture data sets demonstrate the validity of this method.

Text Classification Based on LDA and Semantic Analysis

The quality of text features directly affects the text classification effect, in order to get the text features which have the high contribution to the text classification in class, this paper proposes a text classification method based on LDA model and category semantic similarity method. The method selects text document topic features by the LDA model and calculates the semantic similarity between these features and categories combined with the word vector model. According to the size of similarity, the weight of the text feature is obtained, and the text classification feature selection and text classification are realized. Finally, the feasibility, validity and correctness of the algorithm are verified by experiments.

Multi‐features fusion classification method for texture image

Aiming at the problem of poor classification and recognition rate for distorted texture image based on single texture feature, a classification method of texture image based on multi-features fusion is proposed. First, the corresponding GLCM features, HOG features, and HU moment features were extracted from the segmented texture images. Then, the three feature matrices were cascaded into a new feature matrix, and the principal component analysis method was used to reduce the dimension of the new feature matrix. Finally, the fused feature matrix was inputted to the support vector machine (SVM) for training, so that the final discriminant model was obtained. The model is applied to the classification of distorted texture images and compared with the single texture feature classification method through experiment. The results show that the multi-features fusion classification method improves the classification accuracy of distorted texture images and has better real-time performance.

Detecting Malicious URLs via a Keyword-Based Convolutional Gated-Recurrent-Unit Neural Network

With the continuous development of Web attacks, many web applications have been suffering from various forms of security threats and network attacks. The security detection of URLs has always been the focus of Web security. Many web application resources can be accessed by simply entering an URL or clicking a link in the browser. An attacker can construct various web attacks such as SQL, XSS, and information disclosure by embedding executable code or injecting malicious code into the URL. Therefore, it is necessary to improve the reliability and security of web applications by accurately detecting malicious URLs. This paper designs a convolutional gated-recurrent-unit (GRU) neural network for the detection of malicious URLs detection based on characters as text classification features. Considering that malicious keywords are unique to URLs, a feature representation method of URLs based on malicious keywords is proposed, and a GRU is used in place of the original pooling layer to perform feature acquisition on the time dimension, resulting in high-accuracy multicategory results. The experimental results show that our proposed neural network detection model is very suitable for high-precision classification tasks. Compared with other classification models, the model accuracy rate is above 99.6%. The use of deep learning to classify URLs to identify Web visitors’ intentions has important theoretical and scientific values for Web security research, providing new ideas for intelligent security detection.

Low Level Texture Features for Snore Sound Discrimination.

Snoring is often associated with serious health risks such as obstructive sleep apnea and heart disease and may require targeted surgical interventions. In this regard, research into automatically and unobtrusively analysing the site of blockages that cause snore sounds is growing in popularity. Herein, we investigate the use of low level image texture features in classification of four specific types of snore sounds. Specifically, we explore histogram of local binary patterns (LBP) in dense grid of rectangular regions and histogram of oriented gradients (HOG) extracted from colour spectrograms for snore sound characterisation. Support vector machines with homogeneous mapping are used in the classification stage of the proposed method. Various experimental works are carried out with both LBP and HOG descriptors on the INTERSPEECH ComParE 2017 snoring sub-challenge dataset. Results presented indicate that LBP descriptors are better than the HOG descriptors in snore type detection and fusion of the LBP and HOG descriptors produces stronger results than either individual descriptor. Further, when compared to the challenge baseline and state-of-the-art deep spectrum features, our approach achieved relative percentage increases in unweighted average recall of 23.1% and 8.3% respectively.