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

A rotation invariant texture classification algorithm based on dual-tree complex wavelet transform (DT-CWT) and support vector machines (SVM) is proposed. First, the texture image is transformed by Radon transform to convert the rotation to translation, the rotation invariant feature vector is composed of the energies of the subbands acquired by DT-CWT which is shift invariant to the transformed texture image, the SVM algorithm is used to the texture classification at last. This algorithm is compared with the classifier of probabilistic neural network (PNN) and other rotation invariant texture classification algorithm, the experiment results show that it can improve the classification rate effectively.

Describes a method for classifying textured images using neural networks and discrete wavelet transform (DWT). In this method, a multiresolution analysis is applied to textured images to extract a set of intelligible features. These extracted features, in the form of DWT coefficient matrices, are used as inputs to four different multilayer perceptron (MLP) neural networks and classified. Generalization performance is improved when a locally connected, weight-sharing network topology is utilized, thus drastically decreasing the number of free parameters during training. This architecture takes advantage of the quasi-periodic nature of the textured images. A novel voting network scheme is also employed to achieve a system classification result from the four networks. The efficacy of the algorithm is demonstrated using real-world textured images. >

Despite Gabor filtering has emerged as one of the leading techniques for texture classification, a unifying approach to its adoption has not emerged yet. As it is true for Gabor filter bank, the design of a filter bank consists of the selection of a proper set of values for the filter parameters. In this paper, it is intended to find a set of Gabor filter bank parameters optimized for the performance of texture classification system. The application method is suggested to compute Gabor filter parameters based on Genetic Algorithm (GA). The parameters are optimized according to each group of textures. We tested the proposed method with several texture images using a standard database. The experimental results demonstrate the effectiveness of proposed approach as the overall success is about 97.5%.

2010 17th IEEE International Conference on Image Processing, ICIP 2010, Hong Kong, 26-29 September 2010

This paper investigates the similarities between Gabor filters and the octave-band directional filter bank (OBDFB) with respect to texture classification. Both decompositions can angularly and radially discriminate in the 2-D frequency domain; however, the OB-DFB has a computationally efficient implementation that makes it an attractive alternative to Gabor filters. An analysis of local energy features from Gabor filters and the OBDFB is presented showing similarities between their texture features. Texture classification experiments using both decompositions show comparable classification accuracy.

A novel two-stage wavelet packet feature approach for classification of rotated textured images is discussed. In the first stage, a set of sorted and dominant wavelet packet features is extracted from a texture image and a Mahalanobis distance classifier is employed to output N best classes. In the second stage, another set of wavelet packet features is extracted from the polarised form of the sample texture image and the most dominant wavelet packet features are selected and passed to the radial basis function (RBF) classifier with the N best classes to output the final matched class. Experimental results, based on a large sample data set of twenty distinct natural textures selected from the Brodatz album with different orientations, show that the proposed method outperforms the similar wavelet methods and the other rotation invariant texture classification schemes, and an overall accuracy rate of 91.4% was achieved.

Local Binary Pattern (LBP) has achieved great success in texture classification due to its accuracy and efficiency. Traditional LBP method encodes local features by binarying the difference in local neighborhood and then represents a given image using the histogram of the binary patterns. However, it ignores the directional statistical information. In this paper, some directional statistical features—including the mean and standard deviation of the local absolute difference—are integrated into the feature extraction to improve the classification ability of the extracted features. In order to reduce estimation errors of the local absolute difference, we further utilize the least square estimate technique to optimize the weight and minimize the local absolute difference, which leads to more stable directional features. In addition, a novel rotation invariant texture classification approach is presented. Experimental results on several texture and face datasets show that the proposed approach significantly improves the classification accuracy of the traditional LBP.

Local binary pattern (LBP) is a simple, yet efficient coding model for extracting texture features. To improve texture classification, this paper designs a median sampling regulation, defines a group of gradient LBP (gLBP) descriptors, proposes a training-based feature model mapping method, and then develops a texture classification frame using the multiresolution feature fusion of four gLBP descriptors. Cooperated by median sampling, four descriptors encode a pixel respectively by central gradient, radial gradient, magnitude gradient and tangent gradient to generate initial gLBP patterns. The feature mapping models of gLBP descriptors are constructed by the maximal relative-variation rate (mr2) of rotation-invariant patterns, and then prestored as mapping lookup files. By mapping, initial patterns can be transformed into low-dimensional ones. And then it generates multiresolution texture features via the joint and concatenation of gLBP descriptors on different sampling parameters. A trained nearest neighbor classifier with chi-square distance is applied to classify textures by feature histograms. The experimental results of simulation on five public texture databases show that the proposed method is reliable and efficient in texture classification. In comparison with nine other similar approaches, including two state-of-the-art ones, the proposed method runs faster than most of them and also outperforms all of them in terms of classification accuracy and noise robustness. It achieves higher accuracy and has also better robustness to the Salt&Pepper and Gaussian noise added artificially into texture images.

In this paper, we propose a new feature extraction method, which is robust against rotation and histogram equalization for texture classification. To this end, we introduce the concept of advanced local binary patterns (ALBP), which reflects the local dominant structural characteristics of different kinds of textures. In addition, to extract the global spatial distribution feature of the ALBP patterns, we incooperate ALBP with the aura matrix measure as the second layer to analyze texture images. The proposed method has three novel contributions, (a) The proposed ALBP approach captures the most essential local structure characteristics of texture images (i.e. edges, corners); (b) the proposed method extracts global information by using Aura matrix measure based on the spatial distribution information of the dominant patterns produced by ALBP; and (c) the proposed method is robust to rotation and histogram equalization. The proposed approach has been compared with other widely used texture classification techniques and evaluated by applying classification tests to randomly rotated and histogram equalized images in two different texture databases: Brodatz and CUReT. The experimental results show that the classification accuracy of the proposed method exceeds the ones obtained by other image features.

Texture classification is a classical yet still active topic in computer vision and pattern recognition. Recently, several new texture classification approaches by modeling texture images as distributions over a set of textons have been proposed. These textons are learned as the cluster centers in the image patch feature space using the K-means clustering algorithm. However, the Euclidian distance based the K-means clustering process may not be able to well characterize the intrinsic feature space of texture textons, which if often embedded into a low dimensional manifold. Inspired by the great success of l <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</sub> -norm minimization based sparse representation (SR), in this paper we propose a novel texture classification method via patch-based sparse texton learning. Specifically, the dictionary of textons is learned by applying SR to image patches in the training dataset. The SR coefficients of the test images over the dictionary are used to construct the histograms for texture classification. Experimental results on benchmark database validate the effectiveness of the proposed method.

Sparse representation with multi-manifold analysis for texture classification from few training images

In this paper, we propose a novel feature extraction scheme for texture classification, in which the texture features are extracted by a two-level hybrid scheme, by integrating two statistical techniques of texture analysis. In the first step, the low level features are extracted by the Gabor filters, and they are encoded with the feature map indices, using Kohonen's SOFM algorithm. In the next step, the encoded feature images are processed by the Gabor filters, Gaussian Markov random fields (GMRF), and Grey level co- occurrence matrix (GLCM) methods to extract the high level features. By integrating two methods of texture analysis in a cascaded manner, we obtained the texture features which achieved a high accuracy for the classification of texture patterns. The proposed schemes were tested on the real microtextures, and the Gabor-GMRF scheme achieved 10 percent increase of the recognition rate, compared to the result obtained by the simple Gabor filtering.

A new method for supervised texture classification, denoted by frame texture classification method (FTCM), is proposed. The method is based on a deterministic texture model in which a small image block, taken from a texture region, is modeled as a sparse linear combination of frame elements. FTCM has two phases. In the design phase a frame is trained for each texture class based on given texture example images. The design method is an iterative procedure in which the representation error, given a sparseness constraint, is minimized. In the classification phase each pixel in a test image is labeled by analyzing its spatial neighborhood. This block is represented by each of the frames designed for the texture classes under consideration, and the frame giving the best representation gives the class. The FTCM is applied to nine test images of natural textures commonly used in other texture classification work, yielding excellent overall performance.

This paper presents a new method for texture image classification based on compressed sensing (CS) and feature representation using scale-invariant Bag-of-Words model (SIBW). The issue is discussed from the following three aspects: 1) texture image compression, 2) Scale Invariant Feature Transform (SIFT) feature extraction in compressed domain and 3) classification by using SIBW. The texture classification method was tested on six common classes of texture images, the results show that the excellent performance can be achieved by the proposed approach, and classification accuracy has been greatly improved.

Extracting spatial arrangement of structural textures using projection information