Texture Image Classification Research Articles

PBNet: Combining Transformer and CNN in Passport Background Texture Printing Image Classification

Passport background texture classification has always been an important task in border checks. Current manual methods struggle to achieve satisfactory results in terms of consistency and stability for weakly textured background images. For this reason, this study designs and develops a CNN and Transformer complementary network (PBNet) for passport background texture image classification. We first design two encoders by Transformer and CNN to produce complementary features in the Transformer and CNN domains, respectively. Then, we cross-wisely concatenate these complementary features to propose a feature enhancement module (FEM) for effectively blending them. In addition, we introduce focal loss to relieve the overfitting problem caused by data imbalance. Experimental results show that our PBNet significantly surpasses the state-of-the-art image segmentation models based on CNNs, Transformers, and even Transformer and CNN combined models designed for passport background texture image classification.

Surface texture image classification of carbon/phenolic composites in extreme environments using deep learning

AbstractThe classification of ablation images holds significant practical value in thermal protection structures, as it enables the assessment of heat and corrosion resistance of composites. This paper proposes an image‐based deep learning framework to identify the surface texture of carbon/phenolic composites ablative images. First, ablation experiments and collection of surface texture images of carbon/phenolic composites under different thermal environments were conducted in an electric arc wind tunnel. Then, a deep learning model based on a convolutional neural network (CNN) is developed for ablative image classification. The pre‐trained network is ultimately employed as the input for transfer learning. The network's feature extraction layer is trained using the ImageNet dataset, while the global average pooling addresses specific classification tasks. The test results demonstrate that the proposed method effectively classifies the relatively small surface texture dataset, enhances the classification performance of ablative surface texture with an accuracy of up to 97.6%, and exhibits robustness and generalization capabilities.Highlights The paper proposes a new deep learning classification method for ablative images. A model highly sensitive to small and weak features is built. Transfer learning and data enhancement techniques are introduced into classification.

Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix

Meat Texture Image Classification Using the Haar Wavelet Approach and a Gray-Level Co-Occurrence Matrix

Applications of Multiscale Geometric Analysis in Image Texture Recognition and Classification

Applications of Multiscale Geometric Analysis in Image Texture Recognition and Classification

Unsupervised texture classification of 3D X-ray Micro-computed Tomography images

Characterizing rock proprieties is crucial in the oilfield to evaluate hydrocarbon reserves. Several studies showed a high correlation between rock properties and textures. Therefore, we propose integrating texture information in the images to identify precisely the most representative textures in highly heterogeneous rocks to estimate their properties. First, we implemented a steerable pyramid decomposition to extract the texture features. Then, those parameters were used as input for the Self-organizing map to classify the textures. Finally, by applying several models and comparing their results, we suggested the best approach to implement for texture classification.

From the visibility graph (VG) to a complementary visibility graph (CVG) and an image complementary visibility graph (ICVG): Algorithms and applications

A new algorithm for bridging the gap between time series and networks is proposed in this short paper called the complementary visibility graph (CVG). The visibility graphs (VGs) method makes it easy to fulfill complex network topology modeling, which is effective for nonlinear dynamic analysis. Based on the proposed CVG, an image complementary visibility graph (ICVG) is also proposed. The algorithmic procedure has three steps. First, the texture images were converted into the corresponding ICVG. Then, the feature descriptors of the texture image datasets were extracted from the associated complex network set. Finally, texture image classification can be successfully achieved by using the most popular classifiers. Experimentally validated on the classic datasets Kylberg and KTHTIPS2b. The results show that the proposed ICVG model and cubic support vector machine classifier on the two datasets have classification accuracies of 100.0% and 93.0%, respectively. On the same image datasets, the results are better than most results in the existing literature, easily extending to similar situations. The source code is available at https://github.com/LaifanPei/CVG.

Data Augmentation for Genetic Programming-Driven Late Merging of HOG and Uniform LBP Features for Texture Classification

The categorization of texture images requires the identification and extraction of meaningful keypoints, a crucial step in ensuring the precise representation of textured images. The literature has introduced numerous descriptors in order to detect and capture both local and global texture characteristics. These descriptors vary in their effectiveness depending on the specific application. However, it is generally accepted that they complement each other by compensating for their strengths and weaknesses. Therefore, many approaches focused on combining different types of image descriptors generating robust features that are invariant to image transformations. These solutions mostly focused on one way to deal with information and faced more problems as they need human intervention and a large set of data. The acknowledged benefits of combining multiple types of descriptors are accompanied by challenges specially. These challenges arise due to differences in properties, such as locality and sparsity, as well as the heterogeneity exhibited by generated features. To address this issue comprehensively, we present an approach that benefits from genetic programming techniques to generate and combine two distinct texture classifiers. It incorporates histograms of oriented gradients and local binary patterns descriptors, which capture different textures. To effectively fuse the results of both classifiers, the proposed approach employs a late fusion process along with data augmentation methods while using a limited amount of training data. To evaluate the performance of the proposed approach in texture image classification tasks, we have conducted extensive experiments on six challenging datasets encompassing various variations. We have also investigated its performance in a cross dataset problem where the model has been trained on instances of a dataset before being tested on samples of another dataset. The obtained results clearly demonstrate that the proposed approach surpasses other relevant low-level approaches, as well as existing GP-based and CNN methods specifically designed for describing and classifying textures. Thanks to its ability to simultaneously leverage multiple descriptors, the suggested solution shows a high potential for real-world applications, particularly in handling various image changes with robustness.

High-resolution art recognition of modern packaging design and images based on texture image classification

Abstract First, this paper proposes the EPNN algorithm to recognize the high-resolution art of texture images. Then, a multi-scale multivariate image analysis method for color-textured surface classification is established. Regarding probabilistic neural networks, a differential evolutionary algorithm is proposed to optimize the smoothing parameters of basic probabilistic neural networks. Texture feature extraction involves extracting energy and statistical features of texture images using tree-structured wavelet packet decomposition and statistical-based methods. The image classification is performed by estimating similarity distances and classifying typical feature clusters. The results show that in terms of recognition ability, the proposed EPNN in this paper achieves an average improvement of 0.216 in CCP compared to the LBP method. In terms of classification ability, the proposed multi-scale multivariate image analysis color texture surface classification method in this paper generally achieves more than 10% higher classification ability than LGP in CUReT and Outex texture image libraries.

Perceiving placental ultrasound image texture evolution during pregnancy with normal and adverse outcome through machine learning prism

IntroductionThe objective was to perform placental ultrasound image texture (UPIA) in first (T1), second(T2) and third(T3) trimesters of pregnancy using machine learning( ML). MethodsIn this prospective observational study the 2D placental ultrasound (US) images from 11-14 weeks, 20-24 weeks, and 28-32 weeks were taken. The image data was divided into training, validating, and testing subsets in the ratio of 80%, 10%, and 10%. Three different ML techniques, deep learning, transfer learning, and vision transformer were used for UPIA. ResultsOut of 1008 cases included in the study, 59.5% (600/1008) had a normal outcome. The image texture classification was compared between T1&T2, T2 &T3 and T1&T3 pairs. Using Inception v3 model, to classify T1& T2 images, gave the accuracy, Cohen Kappa score of 83.3%, 0.662 respectively. The image classification between T1&T3 achieved best results using EfficientNetB0 model, having the accuracy, Cohen Kappa score, sensitivity and specificity of 87.5%, 0.749, 83.4%, and 88.9% respectively. Comparison of placental image texture among cases with materno-fetal adverse outcome and controls was done using Efficient Net B0. The F1 score, was found to be 0.824 , 0.820, and 0.892 in T1, T2 and T3 respectively. The sensitivity and specificity of the model was 77.4% at 80.2% at T1 but increased to 81.0% and 93.9% at T2 &T3 respectively. DiscussionThe study presents a novel technique to classify placental ultrasound image texture using ML models and could differentiate first and third-trimester normal placenta and normal and adverse pregnancy outcome images with good accuracy.

A novel technique for texture description and image classification based in RGB compositions

AbstractAt present, facial recognition entertains great importance in performing authentication processes, because it prevents unauthorized access to devices and places. Additionally, it allows for the identification of persons. Henceforth, this paper proposes a novel texture descriptor calledCyclical Chromaand a new classification technique, which takes in consideration the sub‐pixel values of 0–255 for each RGB (Red, Green, Blue) channel that conforms the image. To verify the effectiveness of the proposed techniques, tests were performed with a database of images in a controlled environment and in one under uncontrolled conditions; additionally, Cyclical Chroma was tested with a different classifier, denominated the Multiclass Classifier, and the results were compared against other descriptors, including GLCM, SHDH, LQP, and CCR, demonstrating the effectiveness of the proposed techniques with 100% efficiency with controlled images and 78% effectiveness under uncontrolled conditions prior to the application of an equalization technique, increasing the efficiency to 100%.

Studying the Classification of Texture Images by K-Means of Co-Occurrence Matrix and Confusion Matrix

In this research, a group of gray texture images of the Brodatz database was studied by building the features database of the images using the gray level co-occurrence matrix (GLCM), where the distance between the pixels was one unit and for four angles (0, 45, 90, 135). The k-means classifier was used to classify the images into a group of classes, starting from two to eight classes, and for all angles used in the co-occurrence matrix. The distribution of the images on the classes was compared by comparing every two methods (projection of one class onto another where the distribution of images was uneven, with one category being the dominant one. The classification results were studied for all cases using the confusion matrix between every Two cases or two steps (two different angles and for the same number of classes). The agreement percentage between the classification results and the various methods was calculated.

Learning to Optimize on Riemannian Manifolds.

Many learning tasks are modeled as optimization problems with nonlinear constraints, such as principal component analysis and fitting a Gaussian mixture model. A popular way to solve such problems is resorting to Riemannian optimization algorithms, which yet heavily rely on both human involvement and expert knowledge about Riemannian manifolds. In this paper, we propose a Riemannian meta-optimization method to automatically learn a Riemannian optimizer. We parameterize the Riemannian optimizer by a novel recurrent network and utilize Riemannian operations to ensure that our method is faithful to the geometry of manifolds. The proposed method explores the distribution of the underlying data by minimizing the objective of updated parameters, and hence is capable of learning task-specific optimizations. We introduce a Riemannian implicit differentiation training scheme to achieve efficient training in terms of numerical stability and computational cost. Unlike conventional meta-optimization training schemes that need to differentiate through the whole optimization trajectory, our training scheme is only related to the final two optimization steps. In this way, our training scheme avoids the exploding gradient problem, and significantly reduces the computational load and memory footprint. We discuss experimental results across various constrained problems, including principal component analysis on Grassmann manifolds, face recognition, person re-identification, and texture image classification on Stiefel manifolds, clustering and similarity learning on symmetric positive definite manifolds, and few-shot learning on hyperbolic manifolds.

Monogenic wavelet scattering network for texture image classification

The scattering transform network (STN) has a similar structure as that of a convolutional neural network. We propose a novel Monogenic Wavelet Scattering Network (MWSN) for 2D texture image classification through a cascade of monogenic wavelet filtering with nonlinear modulus and averaging operators by replacing the 2D Morlet wavelet filtering in the standard STN. Our MWSN can extract useful hierarchical and directional features with interpretable coefficients. Using the CUReT texture image database, we demonstrate the superior performance of our MWSN over the standard STN. This performance improvement can be explained by the natural extension of 1D analyticity to 2D monogenicity.

Surface curvature based completed local ternary pattern for texture image classification

Surface curvature based completed local ternary pattern for texture image classification

Retracted: Texture Image Classification Method of Porcelain Fragments Based on Convolutional Neural Network.

[This retracts the article DOI: 10.1155/2021/1823930.].

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

Weighted Constraint Feature Selection of Local Descriptor for Texture Image Classification

Surface curvature based Completed Local Ternary Pattern for Texture Image Classification

Surface curvature based Completed Local Ternary Pattern for Texture Image Classification

Swin-textural: A novel textural features-based image classification model for COVID-19 detection on chest computed tomography

BackgroundChest computed tomography (CT) has a high sensitivity for detecting COVID-19 lung involvement and is widely used for diagnosis and disease monitoring. We proposed a new image classification model, swin-textural, that combined swin-based patch division with textual feature extraction for automated diagnosis of COVID-19 on chest CT images. The main objective of this work is to evaluate the performance of the swin architecture in feature engineering. Material and methodWe used a public dataset comprising 2167, 1247, and 757 (total 4171) transverse chest CT images belonging to 80, 80, and 50 (total 210) subjects with COVID-19, other non-COVID lung conditions, and normal lung findings. In our model, resized 420 × 420 input images were divided using uniform square patches of incremental dimensions, which yielded ten feature extraction layers. At each layer, local binary pattern and local phase quantization operations extracted textural features from individual patches as well as the undivided input image. Iterative neighborhood component analysis was used to select the most informative set of features to form ten selected feature vectors and also used to select the 11th vector from among the top selected feature vectors with accuracy >97.5%. The downstream kNN classifier calculated 11 prediction vectors. From these, iterative hard majority voting generated another nine voted prediction vectors. Finally, the best result among the twenty was determined using a greedy algorithm. ResultsSwin-textural attained 98.71% three-class classification accuracy, outperforming published deep learning models trained on the same dataset. The model has linear time complexity. ConclusionsOur handcrafted computationally lightweight swin-textural model can detect COVID-19 accurately on chest CT images with low misclassification rates. The model can be implemented in hospitals for efficient automated screening of COVID-19 on chest CT images. Moreover, findings demonstrate that our presented swin-textural is a self-organized, highly accurate, and lightweight image classification model and is better than the compared deep learning models for this dataset.

Tongue Image Texture Classification Based on Image Inpainting and Convolutional Neural Network.

Tongue texture analysis is of importance to inspection diagnosis in traditional Chinese medicine (TCM), which has great application and irreplaceable value. The tough and tender classification for tongue image relies mainly on image texture of tongue body. However, texture discontinuity adversely affects the classification of the tough and tender tongue classification. In order to promote the accuracy and robustness of tongue texture analysis, a novel tongue image texture classification method based on image inpainting and convolutional neural network is proposed. Firstly, Gaussian mixture model is applied to separate the tongue coating and body. In order to exclude the interference of tongue coating on tough and tender tongue classification, a tongue body image inpainting model is built based on generative image inpainting with contextual attention to realize the inpainting of the tongue body image to ensure the continuity of texture and color change of tongue body image. Finally, the classification model of the tough and tender tongue inpainting image based on ResNet101 residual network is used to train and test. The experimental results show that the proposed method achieves better classification results compared with the existing methods of texture classification of tongue image and provides a new idea for tough and tender tongue classification.

Texture image classification based on a pseudo-parabolic diffusion model.

This work proposes a novel method based on a pseudo-parabolic diffusion process to be employed for texture recognition. The proposed operator is applied over a range of time scales giving rise to a family of images transformed by nonlinear filters. Therefore each of those images are encoded by a local descriptor (we use local binary patterns for that purpose) and they are summarized by a simple histogram, yielding in this way the image feature vector. Three main novelties are presented in this manuscript: (1) The introduction of a pseudo-parabolic model associated with the signal component of binary patterns to the process of texture recognition and a real-world application to the problem of identifying plant species based on the leaf surface image. (2) We also introduce a simple and efficient discrete pseudo-parabolic differential operator based on finite differences as texture descriptors. While the work in [26] uses complete local binary patterns, here we use the original version of the local binary pattern operator. (3) We also discuss, in more general terms, the possibilities of exploring pseudo-parabolic models for image analysis as they balance two types of processing that are fundamental for pattern recognition, i.e., they smooth undesirable details (possibly noise) at the same time that highlight relevant borders and discontinuities anisotropically. Besides the practical application, the proposed approach is also tested on the classification of well established benchmark texture databases. In both cases, it is compared with several state-of-the-art methodologies employed for texture recognition. Our proposal outperforms those methods in terms of classification accuracy, confirming its competitiveness. The good performance can be justified to a large extent by the ability of the pseudo-parabolic operator to smooth possibly noisy details inside homogeneous regions of the image at the same time that it preserves discontinuities that convey critical information for the object description. Such results also confirm that model-based approaches like the proposed one can still be competitive with the omnipresent learning-based approaches, especially when the user does not have access to a powerful computational structure and a large amount of labeled data for training.