LBP-and-ScatNet-based combined features for efficient texture classification

Multimedia Tools and Applications - In this paper, we propose a micro-macro feature combination approach for texture classification. The two disparate yet complementary categories of features are...     

Invariant and adaptive geometrical texture features for defect detection and classification

Automatic texture defect detection is highly important for many fields of visual inspection. We introduce novel, geometrical texture features for this task, which are Euclidean motion invariant and texture adaptive: An algebraic function (rational, Padé, or polynomial) is integrated over intensities in local, circular neighborhoods on the image including an anisotropical texture analysis. Adaptiveness is achieved through the optimization of this feature kernel and further coefficients based on a simplex energy minimization, constrained by a measure of texture discrimination (Fisher criterion). A backpropagation trained, multilayer perceptron network classifies the textures locally. Our approach contains new properties, usually not common in feature theories: Theoretically implicit, multiple invariances and an automatic and specific adaptation of the features to the texture images. Experiments with a fabric data set and Brodatz textures reveal up to 98.6% recognition accuracy.     

Rotation invariant features for color texture classification and retrieval under varying illumination

This article proposes a new quaternion-based method for rotation invariant color texture classification under illumination variance with respect to direction and spectral band. The color of an object varies according to the spectral power distribution, object-illumination, and viewing geometry of the light source. The quaternion representation of color is shown to be effective, which treats color channels as single unit rather than separate components. New texture signatures are extracted by calculating the norm of the Quaternion fourier spectrum. These signatures are proved to be invariant under image rotation and illumination rotation. Moreover, these features are also invariant to the color spaces. The robustness of different color spaces against varying illumination in color Texture classification with 45 samples of 15 outex texture classes are examined. Comparative results show that the proposed method is efficient in rotation invariant texture classification.     

Effective texture classification by texton encoding induced statistical features

Effective and efficient texture feature extraction and classification is an important problem in image understanding and recognition. Recently, texton learning based texture classification approaches have been widely studied, where the textons are usually learned via K-means clustering or sparse coding methods. However, the K-means clustering is too coarse to characterize the complex feature space of textures, while sparse texton learning/encoding is time-consuming due to the l₀-norm or l₁-norm minimization. Moreover, these methods mostly compute the texton histogram as the statistical features for classification, which may not be effective enough. This paper presents an effective and efficient texton learning and encoding scheme for texture classification. First, a regularized least square based texton learning method is developed to learn the dictionary of textons class by class. Second, a fast two-step l₂-norm texton encoding method is proposed to code the input texture feature over the concatenated dictionary of all classes. Third, two types of histogram features are defined and computed from the texton encoding outputs: coding coefficients and coding residuals. Finally, the two histogram features are combined for classification via a nearest subspace classifier. Experimental results on the CUReT, KTH_TIPS and UIUC datasets demonstrated that the proposed method is very promising, especially when the number of available training samples is limited.     

A probabilistic SVM approach for hyperspectral image classification using spectral and texture features

New hyperspectral sensors can collect a large number of spectral bands, which provide a capability to distinguish various objects and materials on the earth. However, the accurate classification of these images is still a big challenge. Previous studies demonstrate the effectiveness of combination of spectral data and spatial information for better classification of hyperspectral images. In this article, this approach is followed to propose a novel three-step spectral–spatial method for classification of hyperspectral images. In the first step, Gabor filters are applied for texture feature extraction. In the second step, spectral and texture features are separately classified by a probabilistic Support Vector Machine (SVM) pixel-wise classifier to estimate per-pixel probability. Therefore, two probabilities are obtained for each pixel of the image. In the third step, the total probability is calculated by a linear combination of the previous probabilities on which a control parameter determines the efficacy of each one. As a result, one pixel is assigned to one class which has the highest total probability. This method is performed in multivariate analysis framework (MAF) on which one pixel is represented by a d-dimensional vector, d is the number of spectral or texture features, and in functional data analysis (FDA) on which one pixel is considered as a continuous function. The proposed method is evaluated with different training samples on two hyperspectral data. The combination parameter is experimentally obtained for each hyperspectral data set as well as for each training samples. This parameter adjusts the efficacy of the spectral versus texture information in various areas such as forest, agricultural or urban area to get the best classification accuracy. Experimental results show high performance of the proposed method for hyperspectral image classification. In addition, these results confirm that the proposed method achieves better results in FDA than in MAF. Comparison with some state-of-the-art spectral–spatial classification methods demonstrates that the proposed method can significantly improve classification accuracies.     

Quaternionic wavelets for texture classification

This article proposes a study of the recent quaternionic wavelet transform (QWT) from a practical point of view through a digital image analysis application. Based on a theoretic 2D generalization of the analytic signal leading to a strong 2D signal modeling, this representation uses actual 2D analytic wavelets and yields subbands having a shift-invariant magnitude and a 3-angle phase, using the quaternion algebra.Our experiment furthers the understanding of this quite sophisticated tool, and shows its practical interest by a clear improvement of a famous wavelet application: texture classification. Thanks to coherent multiscale analysis brought by the QWT we obtain better classification results than with standard wavelets in a similar process.     

Feature extraction for texture classification

We address the problem of texture classification. Random walks are simulated for plane domains $\text{A}$ bounded by absorbing boundaries Γ, and the absorption distributions are estimated. Measurements derived from the above distributions are the features used for texture classification. Experiments using such a model have been performed and the results showed a rate of accuracy of 89.7% for a data set consisting of one hundred and twenty-eight textured images equally distributed among thirty-two classes of textures.     

Self-organizing maps for texture classification

A further investigation of our intelligent machine vision system for pattern recognition and texture image classification is discussed in this paper. A data set of 335 texture images is to be classified into several classes, based on their texture similarities, while no a priori human vision expert knowledge about the classes is available. Hence, unsupervised learning and self-organizing maps (SOM) neural networks are used for solving the classification problem. Nevertheless, in some of the experiments, a supervised texture analysis method is also considered for comparison purposes. Four major experiments are conducted: in the first one, classifiers are trained using all the extracted features without any statistical preprocessing; in the second simulation, the available features are normalized before being fed to a classifier; in the third experiment, the trained classifiers use linear transformations of the original features, received after preprocessing with principal component analysis; and in the last one, transforms of the features obtained after applying linear discriminant analysis are used. During the simulation, each test is performed 50 times implementing the proposed algorithm. Results from the employed unsupervised learning, after training, testing, and validation of the SOMs, are analyzed and critically compared with results from other authors.     

Subband effect of the wavelet fuzzy C-means features in texture classification

The wavelet transform is an important analysis used in the field of texture classification. It decomposes an image into subbands. Some of the subbands contain more significant coefficients than others. Based on this property, we propose a texture analysis and classification approach using a combination of the fuzzy C-means clustering method (FCM) and the wavelet transform. By taking the energy coefficients of two pairs of frequency channels resulting from 2D wavelet transform, and grouping the data into a specific number of clusters, we were able to build a feature list for each texture. The feature list is obtained by applying the FCM on each frequency channel pair. The centers obtained are used as the features for every combination of frequency channel pair; the partition matrix generated from the FCM is used as a method for determining the k-nearest neighbors of an unknown texture. The subband effect of the wavelet FCM features is studied by varying the number of decomposition levels of the wavelet tree. Optimal number of features was obtained by varying the number of clusters and the k-nearest neighbors of the FCM. Experiments show that this method outperformed other methods (linear regression model, Gabor transform).     

Multi-class supervised classification of electrical borehole wall images using texture features

Electrical borehole wall images represent micro-resistivity measurements at the borehole wall. The lithology reconstruction is often based on visual interpretation done by geologists. This analysis is very time-consuming and subjective. Different geologists may interpret the data differently. In this work, linear discriminant analysis (LDA) in combination with texture features is used for an automated lithology reconstruction of ODP (Ocean Drilling Program) borehole 1203A drilled during Leg 197. Six rock groups are identified by their textural properties in resistivity data obtained by a Formation MircoScanner (FMS). Although discriminant analysis can be used for multi-class classification, non-optimal decision criteria for certain groups could emerge. For this reason, we use a combination of 2-class (binary) classifiers to increase the overall classification accuracy. The generalization ability of the combined classifiers is evaluated and optimized on a testing dataset where a classification rate of more than 80% for each of the six rock groups is achieved. The combined, trained classifiers are then applied on the whole dataset obtaining a statistical reconstruction of the logged formation. Compared to a single multi-class classifier the combined binary classifiers show better classification results for certain rock groups and more stable results in larger intervals of equal rock type.     

Sum and difference histograms for texture classification

The sum and difference of two random variables with same variances are decorrelated and define the principal axes of their associated joint probability function. Therefore, sum and difference histograms are introduced as an alternative to the usual co-occurrence matrices used for texture analysis. Two maximum likelihood texture classifiers are presented depending on the type of object used for texture characterization (sum and difference histograms or some associated global measures). Experimental results indicate that sum and difference histograms used conjointly are nearly as powerful as cooccurrence matrices for texture discrimination. The advantage of the proposed texture analysis method over the conventional spatial gray level dependence method is the decrease in computation time and memory storage.     

Synergies between texture features: an abstract feature based framework for meningioma subtypes classification

Histopathology is the gold standard for accurate diagnosis of cancer, tumors and similar diseases. Real-world pathological images, due to non-homogeneous nature and unorganized spatial intensity variations, are complex to analyze and classify. The major challenge in classifying pathological images is the complexity due to high intra-class variability and low inter-class variation in texture. Accuracy of histopathological image classification is highly dependent on the relevancy of the selected features to the problem. This paper is an effort in the same direction and presents an abstract feature based framework called abstract feature framework (AFF) to select optimal set of the most relevant features to classify pathological images. An abstract feature is created by identifying interlinked run-length texture features and grouping them. AFF is comprised of a new data structure called Abstract Feature Tree (AFT) and an algorithm for manipulating it. AFT is a tree structure in which nodes are abstract features. The Linkage Learning Algorithm for manipulating AFT is the brain of this framework and inspired by genetic algorithm. It creates better abstract features by first identifying interlinked abstract features and then combining them. This process is repeated until no improvement is found. On termination, the final list of abstract features is used for classifying pathological images. The proposed framework was tested on real-world histopathological meningioma dataset. Results obtained proved that the proposed framework outperformed the best-known rank-based feature selection techniques by using, on average, approximately three times less features to achieve 22% higher classification accuracy.     

A study of Gaussian mixture models of color and texture features for image classification and segmentation

The aims of this paper are two-fold: to define Gaussian mixture models (GMMs) of colored texture on several feature spaces and to compare the performance of these models in various classification tasks, both with each other and with other models popular in the literature. We construct GMMs over a variety of different color and texture feature spaces, with a view to the retrieval of textured color images from databases. We compare supervised classification results for different choices of color and texture features using the Vistex database, and explore the best set of features and the best GMM configuration for this task. In addition we introduce several methods for combining the ‘color’ and ‘structure’ information in order to improve the classification performances. We then apply the resulting models to the classification of texture databases and to the classification of man-made and natural areas in aerial images. We compare the GMM model with other models in the literature, and show an overall improvement in performance.     

Multiple classifier system using classification confidence for texture classification

Multimedia Tools and Applications - This paper proposes a simple yet effective novel classifier fusion strategy for multi-class texture classification. The resulting classification framework is...     

Improving hyperspectral image classification by combining spectral,texture, and shape features

Several studies have already demonstrated the efficiency of utilizing spatial information in representation and interpretation of hyperspectral (HS) images. Texture and shape features are known as two important categories of spatial information in various applications of image processing. This study tries to utilize texture and shape features extracted from HS images, as well as spectral information, in order to reduce overall classification errors. These features include morphological profiles (MPs), global Gabor features, and features extracted from conventional and segmentation-based grey-level co-occurrence matrices (GLCMs). Various combinations of these spatial features along with spectral information are fed into a support vector machine (SVM) classifier, and the best combinations for different situations are determined. Experiments on the widely used Indian Pines, Pavia University, and Salinas HS data sets demonstrate the efficiency of the proposed framework in comparison with some recent spectral–spatial classification methods.     

Application-independent feature selection for texture classification

Recent developments in texture classification have shown that the proper integration of texture methods from different families leads to significant improvements in terms of classification rate compared to the use of a single family of texture methods. In order to reduce the computational burden of that integration process, a selection stage is necessary. In general, a large number of feature selection techniques have been proposed. However, a specific texture feature selection must be typically applied given a particular set of texture patterns to be classified. This paper describes a new texture feature selection algorithm that is independent of specific classification problems/applications and thus must only be run once given a set of available texture methods. The proposed application-independent selection scheme has been evaluated and compared to previous proposals on both Brodatz compositions and complex real images.