Effect of wavelet bases in texture classification using a tree-structured wavelet transform

This article describes a new method and approch of texture characterization. Using complex network representation of an image, classical and derived (hierarchical) measurements, we presente how to have good performance in texture classification. Image is represented by a complex networks : one pixel as a node. Node degree and clustering coefficient, using with traditionnal and extended hierarchical measurements, are used to characterize ”organisation” of textures.

Texture classification is the assignment of texture to one or more texture classes. It has been largely used in various fields. This paper proposes a system for Texture Classification using Independent Component Analysis (ICA) using set of classifiers. Independent Component Analysis proved its efficiency in many domains. Our objective is to improve texture classification by adopting the use of ICA with a classifier in this domain. After extracting the main features of the image, classification using set of classifiers is performed. Experimental results have shown that ICA has a promising performance in texture classification. When combined with neural networks, Texture classification accuracy reached the accuracy of 91%. Furthermore, Naïve Bayes showed both exceptional training and testing times, and therefore, it proved to be efficient for big datasets.

Spectral Filter Array cameras provide a fast and portable solution for spectral imaging. Texture classification from images captured with such a camera usually happens after a demosaicing process, which makes the classification performance rely on the quality of the demosaicing. This work investigates texture classification methods applied directly to the raw image. We trained a Convolutional Neural Network and compared its classification performance to the Local Binary Pattern method. The experiment is based on real SFA images of the objects of the HyTexiLa database and not on simulated data as are often used. We also investigate the role of integration time and illumination on the performance of the classification methods. The Convolutional Neural Network outperforms other texture classification methods even with a small amount of training data. Additionally, we demonstrated the model’s ability to adapt and scale for different environmental conditions such as illumination and exposure compared to other methods. In order to explain these results, we analyze the extracted features of our method and show the ability of the model to recognize different shapes, patterns, and marks in different textures.

The binary pattern family has drawn wide attention for texture representation due to its promising performance and simple operation. However, most binary pattern methods focus on local neighborhoods but ignore center pixels. Even if some studies introduce the center based sub-pattern to provide complementary information, extant center based sub-patterns are much weaker than other local neighborhood based sub-patterns. This severe unbalance limits the classification performance of fusion features significantly. To alleviate this problem, this paper designs a multiply threshold center pattern (MTCP) to provide a more discriminative and complementary local texture representation with a compact form. First, a multiply thresholds encoding strategy is designed to encode the center pixel that generates three 1-bit binary patterns. Second, it adopts a compact multi-pattern encoding strategy to combine them into the 3-bit MTCP. Furthermore, this paper proposes a completed multiply threshold encoding pattern by fusing the MTCP, local sign pattern, and local magnitude pattern. Comprehensive experimental evaluations on three popular texture classification benchmarks confirm that the completed multiply threshold encoding pattern achieves superior texture classification performance.

An expert system based on Wavelet Neural Network-Adaptive Norm Entropy for scale invariant texture classification

Two critical issues that effect image quality as well as system design in wavelet image coders are the choice of wavelet bases and the quantization/coding strategy. In recent years orthogonal and biorthogonal wavelet bases have been successfully used for image coding. In this paper a class of spline based shift-orthogonal wavelet packets are used to achieve high quality compression at low bit rates. These shift-orthogonal wavelet bases allow the design of shorter wavelet synthesis filters while preserving the orthogonality within the independent wavelet channels. These features can be used for efficient quantization of individual channels that may result in reduction of reconstruction artifacts such as ringing effects at lower bit rates. Moreover by dropping the high frequency channels as requested by our multi-level EZW coding scheme, it is possible to create a highly scalable encoded bit stream.

This paper proposes an SVM ensemble approach for improving textural classification performance. Finding informative patterns in an image texture is an important issue for image classification and remains a challenge. Local spatial pattern mapping (LSPM) method has been proposed for texture classification based on a complex network model. The purpose of the method was manipulating the spatial distribution in an image texture with multi-radial distance analysis. Although the classification performance was improved, there is a limitation by using a single support vector machine (SVM) as a classifier. Accordingly, we propose an SVM ensemble method with Bagging technique to overcome the limitation by showing improved textural classification performance. In experiments, the SVM ensemble classification performance is compared to the single SVM, SVM with cross-validation and k-NN classifiers by using the Brodatz, UIUC and Outex texture databases. As results, the SVM ensemble is shown to be effective for improving textural classification performance as compared to the other classifiers.

With the widespread scope of image processing in fields such as ground classification, segmentation of satellite images, biomedical surface inspection and content-based image retrieval (CBIR), texture analysis is an important domain with a wide scope. The process of texture analysis comprises the following important steps: texture classification, segmentation and synthesis. Texture is a significant property of images which is difficult to define even though the human eye may recognize it with ease. Texture classification has remained an intangible pattern recognition task despite numerous studies. The prime issue in any texture classification application is how to select the features and which features to consider for representing texture. Another major issue remains the type of metric to be used for comparing the feature vectors. The traditional way of texture classification is to convert the texture image into a vector representing the features using a set of filters. This is followed by classification with a few smoothening steps involved in between. This paper outlines a picture of the basic features of an image as texture, colour and shape which are to be extracted to form the feature vector. The concept of applying Machine learning for the purpose of classification is explored and implemented. The use of Soft Computing Technique—Artificial Neural Networks is illustrated for the purpose of classification. The texture features are extracted using the GLCM method and used as input and fed to the neural network. The algorithm used in training the neural networks is the traditional backpropagation algorithm. The results show which configuration of the multi-layer feedforward architecture is best suited according to our experimental set-up.

Convolutional neural networks are global trainable multi-stage architectures that automatically learn translation invariant features from raw input images. However, in tradition they only allow adjacent layers connected, limiting integration of multi-scale information. To further improve their performance in classification, we present a new architecture called shortcut convolutional neural networks. This architecture can concatenate multi-scale feature maps by shortcut connections to form the fully-connected layer that is directly fed to the output layer. We give an investigation of the proposed shortcut convolutional neural networks on gender classification and texture classification. Experimental results show that shortcut convolutional neural networks have better performances than those without shortcut connections, and it is more robust to different settings of pooling schemes, activation functions, initializations, and optimizations.

It is presented a method of texture recognition based on the analysis of multilevel morphological spectra of biomedical images under consideration. The n-th level morphological spectrum of the image is defined as a matrix whose elements have been obtained due to an rearrangement of the results of four standard algebraic operations performed on the components of the former, (n-1)-th level spectrum. The higher is the spectrum-level, the larger are the morphological structures being taken into consideration in texture analysis; the lowest (0-th) level spectrum corresponds to the bitmap of the original image. It is shown that the original image can be reconstructed on the basis of its morphological spectrum of any given level. The set of spectra can be represented in the form of a tree. Morphological spectra corresponding to selected branches of this tree are characteristic for certain classes of textures and, as such, they can be taken into account for effective discrimination and recognition of the given classes of textures.

In this paper, we propose a convolutional neural network (CoNN) for texture classification. This network has the ability to perform feature extraction and classification within the same architecture, whilst preserving the two-dimensional spatial structure of the input image. Feature extraction is performed using shunting inhibitory neurons, whereas the final classification decision is performed using sigmoid neurons. Tested on images from the Brodatz texture database, the proposed network achieves similar or better classification performance as some of the most popular texture classification approaches, namely Gabor filters, wavelets, quadratic mirror filters (QMF) and co-occurrence matrix methods. Furthermore, The CoNN classifier outperforms these techniques when its output is postprocessed with median filtering

In this paper some of the commonly used features for texture classification based on co- occurrence statistics are studied. First, the classification capabilities of individual features in classifying among a small and a large number of texture images are evaluated. Then, the capabilities of different combinations of texture features are examined in order to establish a reduced set of features for maximum performance. An artificial neural network is used to test the suitability of promising feature groups for texture classification. It is shown that the features considered may be broadly divided into two groups in terms of their classification performance. It is also shown that with a judicious choice of features and a well trained neural network classifier, high recognition rates can be achieved.

Grayscale and color textures can have spectral informative content. This spectral information coexists with the grayscale or chromatic spatial pattern that characterizes the texture. This informative and nontextural spectral content can be a source of confusion for rigorous evaluations of the intrinsic textural performance of texture methods. In this paper, we used basic image processing tools to develop a new class of textures in which texture information is the only source of discrimination. Spectral information in this new class of textures contributes only to form texture. The textures are grouped into two databases. The first is the Normalized Brodatz Texture database (NBT) which is a collection of grayscale images. The second is the Multiband Texture (MBT) database which is a collection of color texture images. Thus, this new class of textures is ideal for rigorous comparisons between texture analysis methods based only on their intrinsic performance on texture characterization.

This paper presents anovel method for texture classification and biometric authentication based on a descriptor called the weighted mean-based patterns (WEM). The proposed descriptor has been developed for extracting texture features from a large dataset of hand images, which has been created by the authors. The method uses the distinctive features of finger knuckle print (FKP) for hand image retrieval, which can be used in biometric identity recognition systems. The proposed method also includes a feature selection step for eliminating less important patterns and a weighted distance measure for quantifying the similarity of images. The method uses the support vector machine (SVM) for the classification stage. The proposed method has been tested on the FKP image dataset to evaluate the image retrieval performance, and also on Brodatz, Vistex, and Stex datasets to evaluate the performance for texture classification. Higher performance of the proposed method is demonstrated through comparison with other methods. The proposed method is shown to be sufficiently precise for a variety of applications, including identity recognition and classification.

Multi-scale texture classification from generalized locally orderless images