Texture Classification Techniques Research Articles

Facial Recognition for Criminal Identification using Convolutional Neural Network

The process of identifying and recognising the criminal is the time consuming and difficult task. There are several ways to identify culprits at the crime site, including fingerprinting, DNA matching, and eyewitness testimony. The criminal face identification system will be built on a existing criminal database. The method for identifying a human face using features extrapolated from an image is presented in this study. The technique for identifying a human face using characteristics extrapolated from a picture is presented in this research. It is quite difficult to develop a computer model for recognizing the human face since it is a complicated multidimensional visual representation. The video captured by the camera will be translated into frames as part of the suggested process. To increase detection accuracy, this suggested a Binary Gradient Alignment (BGA) algorithm a description texture classification technique. When a facial feature is detected in an image frame, it undergoes pre-processing to eliminate unnecessary data and reduce unwanted distortions. The real- time processed image is compared to the trained images that have previously been saved in the database. The technology will send an automatic email notice to the police officials if the surveillance camera detects a criminal.

A novel pixel range calculation technique for texture classification

A novel pixel range calculation technique for texture classification

Texture, Morphology, and Statistical Analysis to Differentiate Primary Brain Tumors on Two-Dimensional Magnetic Resonance Imaging Scans Using Artificial Intelligence Techniques.

ObjectivesA primary brain tumor starts to grow from brain cells, and it occurs as a result of errors in the DNA of normal cells. Therefore, this study was carried out to analyze the two-dimensional (2D) texture, morphology, and statistical features of brain tumors and to perform a classification using artificial intelligence (AI) techniques.MethodsAI techniques can help radiologists to diagnose primary brain tumors without using any invasive measurement techniques. In this paper, we focused on deep learning (DL) and machine learning (ML) techniques for texture, morphological, and statistical feature classification of three tumor types (namely, glioma, meningioma, and pituitary). T1-weighted magnetic resonance imaging (MRI) 2D scans were used for analysis and classification (multiclass and binary). A total of 102 features were calculated for each tumor, and the 20 most significant features were selected using the three-step feature selection method, which included removing duplicate features, Pearson correlations, and recursive feature elimination.ResultsFrom the predicted results of multiclass and binary classification, a long short-term memory binary classification (glioma vs. meningioma) showed the best performance, with an average accuracy, recall, precision, F1-score, and kappa coefficient of 97.7%, 97.2%, 97.5%, 97.0%, and 94.7%, respectively.ConclusionsThe early diagnosis of primary brain tumors is very important because it can be the key to effective treatment. Therefore, this research presents a method for early diagnoses by effectively classifying three types of primary brain tumors.

Refined Color Texture Classification Using CNN and Local Binary Pattern

Representation and classification of color texture generate considerable interest within the field of computer vision. Texture classification is a difficult task that assigns unlabeled images or textures to the correct labeled class. Some key factors such as scaling and viewpoint variations and illumination changes make this task challenging. In this paper, we present a new feature extraction technique for color texture classification and recognition. The presented approach aggregates the features extracted from local binary patterns (LBP) and convolution neural network (CNN) to provide discriminatory information, leading to better texture classification results. Almost all of the CNN model cases classify images based on global features that describe the image as a whole to generalize the entire object. LBP classifies images based on local features that describe the image’s key points (image patches). Our analysis shows that using LBP improves the classification task when compared to using CNN only. We test the proposed approach experimentally over three challenging color image datasets (ALOT, CBT, and Outex). The results demonstrated that our approach improved up to 25% in the classification accuracy over the traditional CNN models. We identify optimal combinations for each dataset and obtain high classification rates. The proposed approach is robust, stable, and discriminatory among the three datasets and has shown enhancement in classification and recognition compared to the state-of-the-art method.

Automatic Unsupervised Texture Recognition Framework Using Anisotropic Diffusion-Based Multi-Scale Analysis and Weight-Connected Graph Clustering

A novel unsupervised texture classification technique is proposed in this research work. The proposed method clusters automatically the textures of an image collection in similarity classes whose number is not a priori known. A nonlinear diffusion-based multi-scale texture analysis approach is introduced first. It creates an effective scale-space by using a well-posed anisotropic diffusion filtering model that is proposed and approximated numerically here. A feature extraction process using a bank of circularly symmetric 2D filters is applied at each scale, then a rotation-invariant texture feature vector is achieved for the current image by combining the feature vectors computed at all these scales. Next, a weighted similarity graph, whose vertices correspond to the texture feature vectors and the weights of its edges are obtained from the distances computed between these vectors, is created. A novel weighted graph clustering technique is then applied to this similarity graph, to determine the texture classes. Numerical simulations and method comparisons illustrating the effectiveness of the described framework are also discussed in this work.

Analysis and Classification of SAR Textures Using Information Theory

The use of Bandt–Pompe probability distributions and descriptors of information theory has been presenting satisfactory results with low computational cost in the time series analysis literature <xref ref-type="bibr" rid="ref1" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[1]</xref> – <xref ref-type="bibr" rid="ref3" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">[3]</xref> . However, these tools have limitations when applied to data without time dependency. Given this context, we present a newly proposed technique for texture analysis and classification based on the Bandt–Pompe symbolization for SAR data. It consists of linearizing a 2-D patch of the image using the Hilbert–Peano curve, build an ordinal pattern transition graph that considers the data amplitude encoded into the weight of the edges, obtain a probability distribution function derived from this graph, and compute information theory descriptors (permutation entropy and statistical complexity) from this distribution and use them as features to feed a classifier. The ordinal pattern graph we propose considers that the edges’ weight is related to the absolute difference of observations, which encodes the information about the data amplitude. This modification considers the unfavorable signal-to-noise ratio of SAR images and leads to the characterization of several types of textures. Experiments with data from Munich urban areas, Guatemala forest regions, and Cape Canaveral ocean samples show the effectiveness of our technique in homogeneous areas, achieving satisfactory separability levels. The two descriptors chosen in this work are easy and quick to calculate and are used as input for a k-nearest neighbor classifier. Experiments show that this technique presents results similar to state-of-the-art techniques that employ a much larger number of features and, consequently, impose a higher computational cost.

The Effect of Camera Calibration on Multichannel Texture Classification

Abstract The efficiency of a texture classification procedure depends on the color space in which it is performed. Classification in a perceptually meaningful space requires chromatic coordinates obtained from a calibrated acquisition setup. The authors assess the impact of camera calibration, within a generic color picture acquisition workflow, on the performance of a number of texture classification techniques. An image calibration pipeline is established and applied to a texture database, and the accuracy of the classification algorithms is evaluated for each step. The results show that the most significant step of the workflow is color rendering although the effect is relatively small. Hence precise scene-referred characterization of the raw data from an acquisition camera is not essential for most texture classification tasks. In addition, working with output-referred RGB data is likely to be adequate for the majority of classification tasks.

Utilization of Support Vector Machine and Speeded up Robust Features Extraction in Classifying Fruit Imagery

Indonesia's various types of fruits can be met by the community. Many fruits that contain a source of vitamins are very beneficial to the body, or as an economic source for farmers. It's no wonder that many experts submit discoveries to increase the amount of productivity or just want to experiment with intelligent systems. Intelligent systems are specially designed machines in certain areas to adjust the capabilities made by the creators. This article provides the latest texture classification technique called Speeded up Robust Features (SURF) with the SVM (Support Vector Machine) method. In this concept, the representation of the image data is done by capturing features in the form of keys. SURF uses the determinant of the Hessian matrix to reach the point of interest in which descriptions and classifications are performed. This method delivers superior performance compared to existing methods in terms of processing time, accuracy, and durability. The results showed that the fruit classification by using the extraction of Speeded up Robust Features (SURF) feature and SVM (Support Vector Machine) Classification method is quite maximal and accurate. Result of 3 kinds of classification with SVM kernel function, SVM Gaussian with 72% accuracy, Polynomial SVM with 69.75% accuracy, and Linear SVM with 70.25% accuracy.

Study of statistical methods for texture analysis and their modern evolutions

AbstractTexture analysis is widely performed in the current time as it is considered as an intimate property of the surface. It is widely used in the field of image processing, remote sensing applications, biomedical analysis, document processing, and so on. In this investigation, we present a detailed study of four different methodologies that have been developed for texture classification. These methodologies include gray level cooccurrence matrix (GLCM), local binary pattern (LBP), autocorrelation function (ACF), and histogram pattern. The detailed investigation on these methods suggests that GLCM is best for analyzing the surface texture, land‐use/landcover classification, and satellite data processing. LBP is widely used to analyze the facial features of an individual. The autocorrelation is used to identify the regularity of the textured surface. Finally, through histograms, one can visually identify the changes developed while analyzing the texture of the image data. Furthermore, we present a brief summary for newly developed texture classification techniques such as binary Gabor pattern, local spiking pattern, SRITCSD method, scale inversion, and deep perception models for texture analysis. Some benchmark texture datasets used in image processing are also discussed in this work.

Bleeding Detection in Gastrointestinal Images using Texture Classification and Local Binary Pattern Technique: A Review

Texture analysis has proven to be a breakthrough in many applications of computer image analysis. It has been used for classification or segmentation of images which requires an effective description of image texture. Due to high discriminative power and simplicity of computation, the local binary pattern descriptors have been used for distinguishing different textures and in extracting texture and color in medical images. This paper discusses performance of various texture classification techniques using Contourlet Transform, Discrete Fourier Transform, Local Binary Patterns and Lacunarity analysis. The study reveals that the incorporation of efficient image segmentation, enhancement and texture classification using local binary pattern descriptor detects bleeding region in human intestines precisely.

RGB Inter-Channel Measures for Morphological Color Texture Characterization

The perception of textures is based on high-level features such as symmetry, brightness, color or direction. Texture characterization is a widely studied topic in the image processing community. The normalized volume of morphological series is used as a texture descriptor in RGB images. However, the correlation between different color channels is not exploited with this descriptor. We propose the usage of inter-channel measures in addition to the volume, to enhance the descriptors potential to discriminate textures. The experiments show that standard texture classification techniques increase between 3%–10% in performance when using our descriptor instead of other state of the art descriptors that do not use inter-channel measures.

A Texture Classification Approach Based on the Integrated Optimization for Parameters and Features of Gabor Filter via Hybrid Ant Lion Optimizer

Texture classification is an important topic for many applications in machine vision and image analysis, and Gabor filter is considered one of the most efficient tools for analyzing texture features at multiple orientations and scales. However, the parameter settings of each filter are crucial for obtaining accurate results, and they may not be adaptable to different kinds of texture features. Moreover, there is redundant information included in the process of texture feature extraction that contributes little to the classification. In this paper, a new texture classification technique is detailed. The approach is based on the integrated optimization of the parameters and features of Gabor filter, and obtaining satisfactory parameters and the best feature subset is viewed as a combinatorial optimization problem that can be solved by maximizing the objective function using hybrid ant lion optimizer (HALO). Experimental results, particularly fitness values, demonstrate that HALO is more effective than the other algorithms discussed in this paper, and the optimal parameters and features of Gabor filter are balanced between efficiency and accuracy. The method is feasible, reasonable, and can be utilized for practical applications of texture classification.

Gray level run length matrix based on various illumination normalization techniques for texture classification

Texture classification under varying illumination conditions is one of the most important challenges. This paper presents a new texture classification approach by taking the combinations of robust illumination normalization techniques applied on gray level run length matrix (GLRLM) for texture features extraction. The purpose of selecting the GRLM, as texture descriptor is that, it extracts information of an image from its gray level runs. A set of consecutive, collinear picture points having the same gray level values is considered as a gray level run. The textured materials usually go through a deep change in their images with variations in illumination and camera pose. For instance, keeping all the parameters fixed but just changing the scale and rotation can result in a completely new texture. Hence, change in gray level values also occurred. Dealing with these variations successfully by utilizing GRLM descriptor for texture classification is the main purpose of this paper. In the suggested approach, 2D wavelet, Tan and Triggs (TT) normalization methods are employed to compensate illumination variations. Experimental results on the Brodatz, VisTex, STex and ALOT databases show that the suggested approach improves the performance significantly as compared to the classical GLRLM descriptor.

An Efficient and Scalable Technique for Texture Analysis to Obtain in IR Model

Nowadays the popularity of multimedia contents search is increasing rapidly such as images, videos, and the audio. Among them, the images are much popular for search and data retrieval processes. In literature there are a number of techniques are exist for efficient and precise image retrieval but according to the conclusion of literature the content based techniques are a much promising approach. In content-based image retrieval systems, the image internal descriptors or features are used for representing the image contents. In these descriptors the shape, colour and texture are primary image content descriptors. A number of works are available for different combinations of these features but there is work exist for the texture analysis. In order to recognize the feature based similar objects, the texture feature played an essential role. In this work the texture analysis is a key area of interest, therefore different texture analysis techniques are studied and LBP is selected for further work. Thus the LBP based texture classification technique is proposed and implemented in this work first. In addition of to demonstrate the effectiveness of the proposed texture analysis technique, the application of the approach in content-based image retrieval is also demonstrated. The implementation of the proposed approach is provided using the visual studio technology. After implementation of the technique, the performance of the system is evaluated in terms of precision, recall, and f-measures. Additionally, for finding the computational complexity the time and space complexity is also computed and compared with the traditional LBP based technique. According to the obtained performance, the proposed technique results from more precise results in less resource consumption.

Bone texture characterization for osteoporosis diagnosis using digital radiography.

We introduce texture classification techniques to effectively diagnose osteoporosis in bone radiography data. Osteoporosis is an age-related systemic bone skeletal disorder characterized by low bone mass and bone structure deterioriation that results in increased bone fragility and higher fracture risk. Therefore, early diagnosis can effectively predict fracture risk and prevent the disease. Automated diagnosis from digital radiographs is very challenging since the scans of healthy and osteoporotic subjects show little or no visual differences, and their density histograms mostly overlap. We designed a system to separate healthy from osteoporotic subjects using high-dimensional textural feature representations computed from radiographs. These features were then reduced using feature selection to obtain the more discriminant subset that was finally classified by our methods. The top performing approach yields 79.3% accuracy and 81% area under the ROC over 116 bone radiographs.

Ground-based cloud classification using weighted local binary patterns

Ground-based cloud classification plays an essential role in meteorological research, and, recently, texture classification techniques have been introduced to automate the process. As a typical texture descriptor, local binary patterns (LBP) have emerged as a very powerful tool due to their effective representation ability. However, it neglects the local contrast information of ground-based cloud images, which may hinder the classification performance. We propose a descriptor called weighted local binary patterns (WLBP) for ground-based cloud classification. The proposed WLBP not only inherits the advantages of LBP but also encodes the useful contrast information of local structures. We define the variance of a local patch as a rotation invariant measure and use this measure as an adaptive weight to adjust the contribution of each neighboring pixel in the process of histogram accumulation. The experimental results demonstrate that the proposed WLBP achieves a better performance than the state-of-the-art methods.

TEXTURE CLASSIFICATION BASED ON OVERLAPPED TEXTON CO-OCCURRENCE MATRIX (OTCOM) FEATURES

Abstract: The pattern identification problems such as stone, rock categorization and wood recognition are used texture classification technique due to its valuable usage in it. Generally, texture analysis can be done one of the two ways i.e. statistical and structural approaches. More problems are occurred when working with statistical approaches in texture analysis for texture categorization. One of the most popular statistical approaches is Gray Level Co-occurrence Matrices (GLCM) approach. This approach is used to discriminating different textures in images. This approach gives better accuracy results but this takes high computational cost. Usually, texture analysis method depends upon how the texture features are extracted from the image to characterize image. Whenever a new texture feature is derived it is tested whether it is precisely classifies the textures or not. Texture features are most important for precise and accurate texture classification and also important that the way in which they are extracted and applied. The present paper derived a new co-occurrence matrix based on overlapped textons patterns. The present paper generates overlapped texton patterns and generates co-occurrence matrices derived a new matrix called Overlapped Texton Co-occurrence Matrices (OTCoM) for stone texture classification. The present paper integrates the advantages of co-occurrence matrix and texton image by representing the attribute of co-occurrence. The co-occurrence features extracted from the OTCoM provides complete texture information about a texture image. The proposed method is experimented on Vistex, Brodatz textures, CUReT, Mayang, Paul Brooke, and Google color texture images. The experimental results indicate the proposed method classification performance is superior to that of many existing methods.

AN INTELLIGENT RECOGNITION SYSTEM FOR IDENTIFICATION OF WOOD SPECIES

The recognition of wood species is needed is many a reas like construction industry, furniture manufacturing, etc.,. The wood is traditionally cla ssified by human experts. But human identification of wood type is not accurate and the manual identifica tion is a time consuming process. So in this study, an intelligent recognition for identification of wo od species was developed. This study uses image enhancement as a preprocessing techniques and uses a new method which divides the image into several blocks known as image blocking. Each block is extracted using grey image and edge detection techniques. The Grey-Level Co-occurrence Matrix (GLCM) is used as a texture classification technique. The GLCMs are generated to obtain three features: Entropy, standard deviation and correlation. The classification technique used to c lassify the wood species is correlation. Our experimental results showed that the proposed metho d can increase the recognition rate up to 95%, which is faster and better than existing system whi ch gives 85% recognition rate.

An Efficient Texture Classification Technique Based on Semi Uniform LBP

Local Binary Pattern' (LBP) operator has been proved to be a theoretically simple yet very effective statistical texture descriptor in many areas. One of the main disadvantage of LBP is they are more sensitive to even a simple noise, this causes a change in LBP pattern. A LBP can generate 256 (0 to 255) different patterns. These patterns are grouped into 59 uniform and 197 nonuniform LBP patterns. Most of the researchers have considered only uniform patterns for texture classification due to their small numbers and claiming that most of the textures are dominated by only uniform LBP's. But after a careful, critical and significant study on the literature, ULBP have some shortcomings: they discard some important texture information, suffer much from non-monotonic illumination variation and do not describe the stochastic characteristics of texture efficiently and sensitive to noise. To overcome this, the present paper defined 'Semi Uniform LBP' (SULBP). The proposed

Salient local binary pattern for ground-based cloud classification

Ground-based cloud classification is challenging due to extreme variations in the appearance of clouds under different atmospheric conditions. Texture classification techniques have recently been introduced to deal with this issue. A novel texture descriptor, the salient local binary pattern (SLBP), is proposed for ground-based cloud classification. The SLBP takes advantage of the most frequently occurring patterns (the salient patterns) to capture descriptive information. This feature makes the SLBP robust to noise. Experimental results using ground-based cloud images demonstrate that the proposed method can achieve better results than current state-of-the-art methods.