Extracting spatial arrangement of structural textures using projection information

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.

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.

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

Texture refers to the tactile impression, such as rough, silky, bumpy, and other texture terms. The Grey-Level Co-occurrence Matrix (GLCM) algorithm is widely used in visual images for texture feature extraction, image structure characterization analysis and texture classification. The GLCM can not only give the statistics of pixel gray values occur in an image, but also give multiple characteristics of the images. Since the primate brain, which is constructed with spiking neurons, has excellent performance in terms of image feature extraction, the improved GLCM algorithm is used to train a spiking neural network and also to simulate the brain's ability about extract key information and utilize these extracted feature information to classify different texture image. Experimental results in this article show that this combination of the GLCM and spiking neural network can effectively extract image features, and the texture classification results is also to achieve satisfactory effect.

Image texture is an important part of many types of images, for example medical images. Texture Analysis is the technique that uses measurable features to categorize complex textures. The main goal is to extract discriminative features that are used in different pattern recognition applications and texture categorization. This paper investigates the extraction of most discriminative features for different texture images from the “Colored Brodatz” dataset using two types of image contrast measures, as well as using the statistical moments on five bands (red, green, blue, grey, and black). The Euclidean distance measure is used in the matching step to check the similarity degree. The proposed method was tested on 112 classes of textures. The achieved results showed that the proposed method is accurate and fast concerning classification accuracy with low computational complexity. The achieved Recognition Rate (RR) was 100%.

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. >

Texture classification and segmentation have been studied using various approaches. The mean Grey-Level Co-occurrence Matrix, introduced by the authors, gives statistical features relatively insensitive to rotation and translation. On the other hand, texture analysis based on fractals is an approach that correlates texture coarseness and fractal dimension. By combining the two types of features, the discrimination power increases. The paper introduces the notion of effective fractal dimension which is an adapting fractal dimension to classification of texture and is calculated by elimination of a constant zone which appears in all textured images. In the case of colour images, we proposed a classification method based on minimum distance between the vectors of the effective fractal dimension of the fundamental colour components. The experimental results to classify real land textured images validate that effective fractal dimension offers a grater discrimination of classes than typical fractal distance based on complete box counting algorithm.Keywordstexturefractal dimensionbox-counting algorithmstatistical featuresimage processingtexture classification

Multiscale texture classification and retrieval based on magnitude and phase features of complex wavelet subbands

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.

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.

The local binary pattern (LBP) and its variants have shown the effectiveness in texture images classification, face recognition, and other applications. However, most of these LBP methods only focus on the histogram of LBP patterns and ignore the spatial contextual information between LBP patterns. In this paper, we propose a 2D-LBP method which uses a sliding window to count the weighted occurrence number of the rotation invariant uniform LBP pattern pairs to obtain the spatial contextual information. The multi-resolution 2D-LBP features can also be obtained when the radius of 2D-LBP is changed. At last, a two-stage classifier which acts as an ensemble learning step is followed to achieve an accurate classification by combining the predictions on each 2D-LBP with single resolution. Theoretical proof shows that the proposed 2D-LBP is a general framework and can be integrated on other LBP variants to derive new feature extraction methods. Experimental results show that, the proposed method achieves 99.71%, 97.09%, 98.48%, and 49.00% classification accuracy on the public “Brodatz,” “CUReT,” “UIUC,” and “FMD” texture image databases, respectively. Compared with the original LBP and its variants, the proposed method obtains higher classification accuracy under different cases, and simultaneously owns shorter time complexity.

Image segmentation, the splitting of a multispectral and panchromatic image into groups of homogeneous pixels based on the region of interest(ROI), is a universal step for many advanced image processing and object recognition. Image segmentation essentially affects the overall performance of any automated image analysis system due to utmost importance of its quality. Image segmentation can be performed by recursively splitting the whole image or by merging together a large number of minute regions until a specified condition is satisfied. Thresholding is an old, simple and important method in gray scale image segmentation. In this paper, we have used Shannon’s entropy and proposed a new multilevel thresholding image segmentation method based on minimization of bi-entropy function. A smoothing technique based on weight value of the pixel within a w × w moving window is introduced to make the splitting result continuous and qualitative. The proposed algorithm takes full account of the spatial information and the gray information to decrease the computing quantity. Standard medical images, texture images, and remote sensing images are segmented in the experiment and compared with other related segmentation methods with different measures. Experimental results show that the proposed method can quickly converge with high computational efficiency.

Previous works about spatial information incorporation into a traditional bag-of-visual-words (BOVW) model mainly consider the spatial arrangement of an image, ignoring the rich textural information in land-use remote-sensing images. Hence, this article presents a 2-D wavelet decomposition (WD)-based BOVW model for land-use scene classification, since the 2-D wavelet decomposition method does well not only in textural feature extraction, but also in the multi-resolution representation of an image, which is favourable for the use of both spatial arrangement and textural information in land-use images. The proposed method exploits the textural structures of an image with colour information transformed into greyscale. Moreover, it works first by decomposing the greyscale image into different sub-images using 2-D discrete wavelet transform (DWT) and then by extracting local features of the greyscale image and all the decomposed images with dense regions in which a given image is evenly sampled by a regular grid with a specified grid space. After that, the method generates the corresponding visual vocabularies and computes histograms of visual word occurrences of local features found in each former image. Specifically, the soft-assignment or multi-assignment (MA) technique is employed, accounting for the impact of clustering on visual vocabulary creation that two similar image patches may be clustered into different clusters when increasing the size of visual vocabulary. The proposed method is evaluated on a ground truth image dataset of 21 land-use classes manually extracted from high-resolution remote-sensing images. Experimental results demonstrate that the proposed method significantly outperforms previous methods, such as the traditional BOVW model, the spatial pyramid representation-based BOVW method, the multi-resolution representation-based BOVW method, and so on, and even exceeds the best result obtained from the creator of the land-use dataset. Therefore, the proposed approach is very suitable for land-use scene classification tasks.

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

The purpose of this study is to develop a dissimilarity measure for the classification of trabecular bone (TB) texture in knee radiographs. Problems associated with the traditional extraction and selection of texture features and with the invariance to imaging conditions such as image size, anisotropy, noise, blur, exposure, magnification, and projection angle were addressed. In the method developed, called a signature dissimilarity measure (SDM), a sum of earth mover's distances calculated for roughness and orientation signatures is used to quantify dissimilarities between textures. Scale-space theory was used to ensure scale and rotation invariance. The effects of image size, anisotropy, noise, and blur on the SDM developed were studied using computer generated fractal texture images. The invariance of the measure to image exposure, magnification, and projection angle was studied using x-ray images of human tibia head. For the studies, Mann-Whitney tests with significance level of 0.01 were used. A comparison study between the performances of a SDM based classification system and other two systems in the classification of Brodatz textures and the detection of knee osteoarthritis (OA) were conducted. The other systems are based on weighted neighbor distance using compound hierarchy of algorithms representing morphology (WND-CHARM) and local binary patterns (LBP). Results obtained indicate that the SDM developed is invariant to image exposure (2.5-30 mA s), magnification (x1.00 - x1.35), noise associated with film graininess and quantum mottle (< 25%), blur generated by a sharp film screen, and image size (> 64 x 64 pixels). However, the measure is sensitive to changes in projection angle (> 5 degrees), image anisotropy (> 30 degrees), and blur generated by a regular film screen. For the classification of Brodatz textures, the SDM based system produced comparable results to the LBP system. For the detection of knee OA, the SDM based system achieved 78.8% classification accuracy and outperformed the WND-CHARM system (64.2%). The SDM is well suited for the classification of TB texture images in knee OA detection and may be useful for the texture classification of medical images in general.