Coordinated Clusters Representation Research Articles

Defect detection on patterned fabrics using texture periodicity and the coordinated clusters representation

Patterned fabrics may be regarded as periodic textures, which are defined as the regular tessellation of a primitive unit. A patterned fabric is considered as defective when a primitive unit is different from the others. In this paper, we propose a one-class classifier that uses Reduced Coordinated Cluster Representation (RCCR) as features. In the training step, the size of the primitive unit of defect-free fabrics is automatically estimated using a texture periodicity algorithm. After that, the fabrics are split into samples of one unit and their local structure is learnt with the RCCR features in a one-class classifier. During the test step, defective and non-defective fabrics are also split into samples and are analyzed unit by unit. If the features of a given unit do not satisfy the classification criterion, it is considered to be a defect. Among the advantages of the RCCR is that it represents structural information of textures in a low-dimensional feature space with high discrimination performance. Results from experiments on an extensive database of real fabric images show that our method yields accurate detections, outperforming other state-of-the-art algorithms.

A Neuro-Fuzzy based System for Classification of Natural Textures

A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. In this paper, two issues are being addressed; one is the extraction of texture features from the fuzzy texture spectrum in the chromatic and achromatic domains from each colour component histogram of natural texture images and the second issue is the concept of a fusion of multiple classifiers. The implementation of an advanced neuro-fuzzy learning scheme has been also adopted in this paper. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification, when compared with other works.

Complexity reduced coding of binary pattern units in image classification

The ability to simulate and control complex physical situations in real time is an important element of many engineering and robotics applications, including pattern recognition and image classification. One of the ways to meet specific requirements of a process is a reduction of computational complexity of algorithms. In this work we propose a new coding of binary pattern units (BPU) that reduces the time and spatial complexity of algorithms of image classification significantly. We apply this coding to a particular but important case of the coordinated clusters representation (CCR) of images. This algorithm reduces the dimension of the CCR feature space and, as a consequence, the time and space complexity of the CCR based methods of image classification, exponentially. In addition, the new coding preserves all the fundamental properties of the CCR that are successfully used in the recognition, classification and segmentation of texture images. The same approach to the coding of BPUs can be used in the Local Binary Pattern (LBP) method. In order to evaluate the reduction of time and space complexity, we did an experiment on multiclass classification of images using the “traditional” and the new coding of the CCR. This test showed very effective reduction of the computing time and required computer memory with the use of the new coding of BPUs of the CCR, retaining 100% or a little less efficiency of classification at the time.

Evaluation of robustness against rotation of LBP, CCR and ILBP features in granite texture classification

The aim of the paper is to conduct a performance evaluation where several texture descriptors such as Local Binary Patterns (LBP), Coordinated Clusters Representation (CCR) and (Improved Local Binary Patterns) ILBP are applied for granite texture classification. In our work we were particularly interested to assess the robustness of the analysed texture descriptors to image rotation when they were implemented in both the standard and rotation-invariant forms. In order to attain this goal, we have generated a database of granite textures that were rotated using hardware and software procedures. The experimental data indicate that the ILBP features return improved performance when compared with those achieved by the LBP and CCR descriptors. Another important finding resulting from this investigation reveals that the classification results obtained when the texture analysis techniques were applied to granite image data rotated by software procedures are inconsistent with those achieved when the hardware-rotated data are used for classification purposes. This discovery is surprising and suggests that the results obtained when the texture analysis techniques are evaluated on synthetically rotated data need to be interpreted with care, as the principal characteristics of the texture are altered by the data interpolation that is applied during the image rotation process.

Rotation-invariant colour texture classification through multilayer CCR

The Coordinated Clusters Representation (CCR) is a texture descriptor based on the probability of occurrence of elementary binary patterns (texels) defined over a square window. The CCR was originally proposed for binary textures, and it was later extended to grayscale texture images through global image thresholding. The required global binarization is a critical point of the method, since this preprocessing stage can wipe out textural information. Another important drawback of the original CCR model is its sensitivity against rotation. In this paper we present a rotation-invariant CCR-based model for colour textures which yields a twofold improvement over the grayscale CCR: first, the use of rotation-invariant texels makes the model insensitive against rotation; secondly, the new texture model benefits from colour information and does not need global thresholding. The basic idea of the method is to describe the textural and colour content of an image by splitting the original colour image into a stack of binary images, each one representing a colour of a predefined palette. The binary layers are characterized by the probability of occurrence of rotation-invariant texels, and the overall feature vector is obtained by concatenating the histograms computed for each layer. In order to quantitatively assess our approach, we performed experiments over two datasets of colour texture images using five different colour spaces. The obtained results show robust invariance against rotation and a marked increase in classification accuracy with respect to grayscale versions of CCR and LBP.

Algorithm for training the minimum error one-class classifier of images

We propose a training algorithm for one-class classifiers in order to minimize the classification error. The aim is to choose the optimal value of the slack parameter, which controls the selectiveness of a classifier. The one-class classifier based on the coordinated clusters representation of images is trained and then used for the classification of texture images. As the slack parameter C varies through a range of values, for each C, the misclassification rate is computed using only the training samples. The value of C that yields the minimum misclassification rate, estimated over the training set, is taken as the optimal value, C(opt). Finally, the optimized classifier is tested on the extended database of images. Experimental results demonstrate the validity of the proposed method. In our experiments, classification efficiency approaches, or is equal to, 100%, after the optimal training of the classifier.

Quantifying a similarity of classes of texture images

To quantify the concept of similarity between classes of images three measures and algorithms of calculation are proposed. The first measure is calculated through the frequency of misclassification of subimages sampled randomly from images. The second one is calculated through the cross membership of the mass center of a class in a feature space. The third measure is defined through the membership of subimages, using the distance between each subimage and the mass center of a class in a feature space. We study these measures, classifying images in the coordinated clusters representation (CCR) feature space with the minimum distance classifier. A database of images of Rosa Porriño granite tiles, previously classified by three human experts, is used in the experiments. The calculated similarity between classes is in excellent accordance with the qualitative evaluation by the human experts.

Image scale determination for optimal texture classification using coordinated clusters representation

The efficiency of texture image classification is certainly influenced by image scale when a feature space or a classification method is not scale invariant. An alternative approach to the scale-invariant techniques is proposed that first estimates an effective image scale and then uses it to adjust texture features to get the best possible texture image recognition and classification. We use the correlation distance between pixels as a measure of the scale of texture images. We study the performance of classification of texture images in the coordinated cluster representation (CCR) versus an image scale and the size of the scanning window used for the coordinated cluster transform. Given the number of classes to be classified in, we find that an optimal (up to 100%) classification efficiency in the CCR feature space is obtained by changing an image scale and/or the size of the scanning window in the coordinated cluster transform.

One-class texture classifier in the CCR feature space

A one-class classifier in the coordinated clusters representation (CCR) feature space is proposed in order to form classes with a required homogeneity with respect to given features of texture images. Experiments with gray level texture images of Brodatz catalog and “Rosa Porriño” polished granites demonstrate high discrimination performance of the criterion. The compactness of a class of images in the CCR feature space is determined by the class variance and, in addition, controlled by an empirical adjustment parameter.

A framework for texture classification using the coordinated clusters representation

A statistical approach based on the coordinated clusters representation of images is used for classification and recognition of textured images. The ability of the descriptor to capture spatial statistical features of an image is exploited. A binarization needed for image preprocessing is done using, but not restricted to, a fuzzy clustering algorithm. A normalized spectrum histogram of the coordinated cluster representation is used as a unique feature vector, and a simple minimum distance classifier is used for classification purposes. Using the size and the number of subimages for prototype generation and the size of the test images as the parameters in the learning and recognition phases, we establish the regions of reliable classification in the space of subimage parameters. The results of classification tests show the high performance of the proposed method that may have industrial application for texture classification.