Brodatz Album Research Articles

HYBRID TEXTURE IDENTIFICATION METHOD

The importance of the modeling mode in systems of computer visual pattern recognition is shown. The purpose of the mode is to determine the types of textures that are present on the images processed in intelligent diagnostic systems. Images processed in technical diagnostic systems contain texture regions, which can be represented by different types of textures -spectral, statistical and spectral-statistical. Texture identification methods, such as, statistical, spectral, expert, multifractal, which are used to identify and analyze texture images, have been analyzed. To determine texture regions on images that are of a combined spectral-statistical nature, a hybrid texture identification method has been developed which makes it possible to take into account the local characteristics of the texture based on multifractal indicators characterizing the non-stationarity and impulsite of the data and the sign of the spectral texture. The stages of the developed hybrid texture identification method are: preprocessing; formation of the primary features vector; formation of the secondary features vector. The formation of the primary features vector is performed for the selected rectangular fragment of the image, in which the multifractal features and the spectral texture feature are calculated. To reduce the feature space at the stage of formation of the secondary identification vector, the principal component method was used. An experimental study of the developed hybrid texture identification method textures on model images of spectral, statistical, spectral-statistical textures has been carried out. The results of the study showed that the developed method made it possible to increase the probability of correct determination of the region of the combined spectral-statistical texture. The developed identification method was tested on images from Brodatz album of textures and images of wear zones of cutting tools, which are processed in intelligent systems of technical diagnostics. The probability of correctly identifying areas of spectral-statistical texture in the images of wear zones of cutting tools averaged 0.9, which is sufficient for the needs of practice.

Gaussian derivative models and ensemble extreme learning machine for texture image classification

Abstract In this paper, we propose an innovative classification method which combines texture features of images filtered by Gaussian derivative models with extreme learning machine (ELM). In the texture image classification, feature extraction is a very crucial step. Thusly, we use linear filters consisting of two Gaussian derivative models, difference of Gaussian (DOG) and difference of offset Gaussian (DOOG), to detect texture information of images. Besides, ensemble extreme learning machine (E2LM) is proposed to reduce the randomness of original ELM and used as the classifier in this paper. We evaluate the performance of both the texture features and the classifier E2LM by using three datasets: Brodatz album, VisTex database and Berkeley image segmentation database. Experimental results indicate that Gaussian derivative models are superior to Gabor filters, and E2LM outperforms the support vector machine (SVM) and ELM in classification accuracy.

A Computational Model for Texture Analysis in Images with Fractional Differential Filter for Texture Detection

This paper is dedicated to the modelling of textured images influenced by fractional derivatives for texture detection. As most of the images contain textures, texture analysis becomes the most important for image understanding and it is a key solution for many computer vision applications. Hence, texture must be suitably detected and represented. Nevertheless, existing texture detection algorithms consider texture as a unique feature from edges. The proposed model explores a novel way of developing texture detection algorithm by mimicking edge detection algorithms. The method assumes that texture feature is analogous to edges and thus, the time complexity is reduced significantly. The model proposed in this work is based on Gaussian kernel smoothing, Fractional partial derivatives and a statistical approach. It is justified to be robust to noisy images and possesses statistical interpretation. The model is validated by the classification experiments on different types of textured images from Brodatz album. It achieves higher classification accuracy than the existing methods.

Principal curvatures based rotation invariant algorithms for efficient texture classification

The histograms of oriented gradients (HOG) and co-occurrence HOG (CoHOG) algorithms are simple and intuitive descriptors. However, the HOG and CoHOG algorithms based on gradient computation still have some shortcomings: they ignore meaningful textural properties and are unstable to noise. In this paper, two new efficient HOG and CoHOG methods are proposed. The proposed algorithms are based on the Gaussian derivative filters, and the feature vectors are obtained by means of principal curvatures. The feature vectors are rotation invariant by means of the rotation invariance characteristic of principal curvatures (i.e. eigenvalues). The experimental results on the CUReT, KTH-TIPS, KTH-TIPS2-a, UIUC, Brodatz album, Kylberg and Xu datasets confirm that the developed algorithms have higher classification rates than state-of-the-art texture classification methods. The classification results also demonstrate that the developed algorithms are more stable to noise and rotation than the original HOG and CoHOG algorithms.

A new approach for texture segmentation based on NBP method

Nowadays, image processing is an interesting research area due to the growth of the communication technologies. Matching problem, which consists of localizing one texture in an image, that contains several textures is one of the fundamental problem of image processing and pattern recognition. In this paper, a new feature extraction method and texture segmentation system are proposed. The proposed method (RINBP) is robust against rotation and improves the ability of extracting the local information. The segmentation architecture follows several steps. First, fixing a converging point ?. After that, a Main analysis Window (MW) starting from ? to the bottom left corner of the image is determined. Then, several possible windows are extracted and the feature extraction method is applied on each window. Finally, a similarity measure is calculated in order to decide if this window is pertinent or not. This process is stopped until the size of the MW reaches a minimum size. Each pertinent window increases the relevance of the desired texture in the output image. Finally, an image of relevance is obtained by considering the most relevant area. For the experiments, textured images generated from Brodatz album database are used. The experiments have shown the superiority of our method compared to other existing methods. The obtained results have illustrated the robustness and the efficiency of the proposed segmentation method based on the relevance of the analysis windows.

Atexture Classification Using Random Forest And Decision Tree

Texture analysis is considered fundamental and important in the fields of pattern recognition, computer vision and image processing. Analysis of the textures involves texture features extraction and selection, and plays an essential role in the classification and segmentation of textural features. In this study have compared two texture classification methods based on the Random Forest (RF) and Decision Tree (DT) classifiers by using a combination method between various extraction features, such as bi-orthogonal wavelet transform, gray level histogram and edge detection. Experiments were conducted on two different databases. The first texture database captured digital images for testing multi-class machining processes, and the second database was collected from the Brodatz album. The results have revealed that RF and DT have yielded higher classification precision.

TEXTURE CLUSTERING OF SATELLITE IMAGES USING SELF-ORGANIZING NEURAL NETWORK

The goal of this paper is to present a texture clustering system for remote sensing image data. Texture information is useful for image data browsing and retrieval. Authors present the results of self-organizing neural network design for solving the clustering task of gray scale remote sensing image data. The architecture of neural network and the learning algorithms for this network such as: algorithm WTA (Winner Takes All), algorithm CWTA (Winner Takes All with Conscience) and classic Kohonen algorithm WTM (Winner Takes Most - the Winner receives more) are considered. Some experimental results using textures of the Brodatz album, multi-spectral and radar images are also represented.

Rotation and Scale Invariant Texture Classification Using Wavelet Transform and LBP Operator

Local Binary Patterns (LBP) is a local approach widely used in the field of texture analysis. Generally, the LBP algorithm is applied on the original texture. Our contribution, as presented in this paper, will be to apply this algorithm on the sub bands resulting from the wavelet transform. This allows characterising texture on various resolution levels. As training bases, we used a set of 30 elements extracted from the Brodatz album and a set of 40 elements extracted from the Vistex album. To test the invariance of the proposed method, several tests have been carried out on textures with rotation changes or scale changes, and many parameters have been tested including the radius of the LBP, the distance measure and the wavelet's nature. These results demonstrate the effectiveness of our characterization method in texture image classification experiments.

Texture analysis using volume-radius fractal dimension

Texture plays an important role in computer vision. It is one of the most important visual attributes used in image analysis, once it provides information about pixel organization at different regions of the image. This paper presents a novel approach for texture characterization, based on complexity analysis. The proposed approach expands the idea of the Mass–radius fractal dimension, a method originally developed for shape analysis, to a set of coordinates in 3D-space that represents the texture under analysis in a signature able to characterize efficiently different texture classes in terms of complexity. An experiment using images from the Brodatz album illustrates the method performance.

Contourlet-1.3 Texture Retrieval Algorithm by Sub-Bands Energy and Consistency

Contourlet-1.3 transform has fewer artifacts than original contourlet transform proposed by Do in 2002; it can extract image texture information more efficiently and has been studied for image de-noising, enhancement, and retrieval situations. Focus on improving the retrieval rate of contourlet-1.3 transform retrieval system, a new contourlet-1.3 texture retrieval algorithm was proposed in this paper. The feature vector of this system was a combination of sub-band energy and consistency and the similarity measure function used here was Canberra distance. Experimental results on 109 texture images coming from Brodatz album show that using the new features can make a higher retrieval rate than the combination of standard deviation and energy which is most commonly used today under the same retrieval time and system structure.

Continuous rotation invariant local descriptors for texton dictionary-based texture classification

Texton dictionary-based texture representation approaches have been proven to be effective for texture classification. We propose two types of local descriptors based on Gaussian derivatives filters, both of them have the property of continuous rotation invariance. The first descriptor directly uses the maximum of the filter responses named continuous maximum responses (CMR). The second descriptor rectifies the filter responses to calculate principal curvatures (PC) of the image surface. The texton dictionary is learned from the training images by clustering the local descriptors, and the representation of each image is the frequency histogram of the textons. The classification results compared with some other popular methods on the CUReT, KTH-TIPS and KTH-TIPS2-a datasets show that representation based on CMR achieves best classification result on the CUReT dataset. The representation based on PC achieves the best classification results on the KTH-TIPS and KTH-TIPS2-a datasets, and the classification performance is robust on different datasets. The experiments of rotation invariant analysis implemented on the Brodatz album illustrate that the CMR descriptor has good inter-class distinguish ability and PC descriptor has strong intra-class congregate ability. The results demonstrate that the proposed local descriptors achieve remarkable performance to classify the rotated textures.

Geometry-Invariant Texture Retrieval Using a Dual-Output Pulse-Coupled Neural Network

This letter proposes a novel dual-output pulse coupled neural network model (DPCNN). The new model is applied to obtain a more stable texture description in the face of the geometric transformation. Time series, which are computed from output binary images of DPCNN, are employed as translation-, rotation-, scale-, and distortion-invariant texture features. In the experiments, DPCNN has been well tested by using Brodatz's album and the VisTex database. Several existing models are compared with the proposed DPCNN model. The experimental results, based on different testing data sets for images with different translations, orientations, scales, and affine transformations, show that our proposed model outperforms existing models in geometry-invariant texture retrieval. Furthermore, the robustness of DPCNN to noisy data is examined in the experiments.

Comparative study of texture detection and classification algorithms

A description and results of application of the computer system PETRA (performance evaluation of texture recognition algorithms) are given. This system is designed for the comparative study of texture analysis algorithms; it includes a database of textured images and a collection of software implementations of texture analysis algorithms. The functional capabilities of the system are illustrated using texture classification examples. Test examples are taken from the Brodatz album, MeasTech database, and a set of aerospace images. Results of a comparative evaluation of five well-known texture analysis methods are described—Gabor filters, Laws masks, ring/wedge filters, gray-level cooccurrence matrices (GLCMs), and autoregression image model.

Rotation and gray-scale-invariant texture analysis using radon and differential radon transforms based hidden Markov models

This study carries out rotation and gray-scale-invariant texture analysis of the textures in Brodatz album. A radon and differential radon transform based technique has been proposed to extract the features of the different textures at different orientations. These features have been used to train one-dimensional hidden Markov models – one for each texture. Testing and classification was done using percentage of correct classification (PCC) as figure of merit. The best percentage achieved was 99.9%.

A New Approach to Rotation Invariant Texture Analysis Based on Radon Transform

In this paper, we propose a new approach to rotation invariant texture analysis. This method uses the Radon transform with some considerations in direction estimation of textural images. Furthermore, it utilizes the information obtained from the number of peaks in the variance array of the Radon transform as a realty feature. The textural features are then generated after rotation of texture along principle direction. Also, to eliminating the introduced error due to rotation of texture, a simple technique is presented. Experimental results on a set of images from the Brodatz album show a good performance achieved by the proposed method in comparison with some recent texture analysis methods.

New Rotation-Invariant Texture Analysis Technique Using Radon Transform and Hidden Markov Models

A rotation invariant texture analysis technique is proposed with a novel combination of Radon Transform (RT) and Hidden Markov Models (HMM). Features of any texture are extracted during RT which due to its inherent property captures all the directional properties of a certain texture. HMMs are used for classification purpose. One HMM is trained for each texture on its feature vector which preserves the rotational invariance of feature vector in a more compact and useful form. Once all the HMMs have been trained, testing is done by picking any of these textures at any arbitrary orientation. The best percentage of correct classification (PCC) is above 98 % carried out on sixty texture of Brodatz album.

Efficient rotation- and scale-invariant texture classification method based on Gabor wavelets

An efficient texture classification method is proposed that considers the effects of both the rotation and scale of texture im- ages. In our method, the Gabor wavelets are adopted to extract local features of an image and the statistical properties of its gray- level intensities are used to represent the global features. Then, an adaptive, circular orientation normalization scheme is proposed to make the feature invariant to rotation, and an elastic cross- frequency searching mechanism is devised to reduce the effect of scaling. Our method is evaluated based on the Brodatz album and the Outex database, and the experimental results show that it out- performs the traditional algorithms. © 2008 SPIE and

Texture features based on local Fourier histogram: self-compensation against rotation

We present a method of introducing rotation invariance in texture features based on a local Fourier histogram (LFH) computed using a 1-D discrete Fourier transform (DFT). To compensate for image rotation, a local image-gradient angle at each image pixel is found from within one of the 1-D DFT coefficients. The rotation invariance is established theoretically, analytically as well as empirically. The rotation-compensated features extracted from the same texture image oriented at different angles exhibit very high cross correlation. Therefore, the proposed texture features are expected to yield very high accuracies for a variety of image data and applications. The improved LFH-based features outperform the earlier version of the features and the features based on Gabor filters in texture recognition on 8560 images from the Brodatz album.

An Innovative Technique of Texture Classification and Comparison Based on Long Linear Patterns

The present paper proposes a method of texture classification based on long linear patterns. Linear patterns of long size are bright features defined by morphological properties: linearity, connectivity, width and by a specific Gaussian-like profile whose curvature varies smoothly along the crest line. The most significant information of a texture often appears in the occurrence of grain components. That’s why the present paper used sum of occurrence of grain components for feature extraction. The features are constructed from the different combination of long linear patterns with different orientations. These features offer a better discriminating strategy for texture classification. Further, the distance function captured from the sum of occurrence of grain components of textures is expected to enhance the class seperability power. The class seperability power of these features is investigated in the classification experiments with arbitrarily chosen texture images taken from the Brodatz album. The experimental results indicated good analysis, and how the classification of textures will be effected with different long linear patterns.

Texture Analysis Using Modified Discrete Radon Transform

In this paper, we address the problem of the rotation-invariant texture analysis. For this purpose, we first present a modified version of the discrete Radon transform whose performance, including accuracy and processing time, is significantly better than the conventional transform in direction estimation and categorization of textural images. We then utilize this transform with a rotated version of Gabor filters to propose a new scheme for texture classification. Experimental results on a set of images from the Brodatz album indicate that the proposed scheme outperforms previous works.