Color Texture Classification Research Articles

Color texture classification based on gravitational collapse

Texture and color are essential attributes to be analyzed for any robust computer vision system. This paper presents a novel method to analyze color-texture images, based on representing states of a simplified gravitational collapse from each image color channel and extracting information from each state using the Bouligand–Minkowski fractal dimension and the lacunarity method. In this approach, we obtained the best classification results when the images of each channel evolved in times t={1,5,10,15}, each time representing a state, using radius r={3,4,5,6} for the Bouligand–Minkowski method and box size l={2,3,4,5,6} for the lacunarity method. The best classification results were 99.37% and 96.57% of success rate (percentage of samples correctly classified) for VisTex and USPTex databases, respectively. These results prove that the proposed approach opens a promising source of research in color texture analysis still to be explored.

Colour texture classification from colour filter array images using various colour spaces

This study focuses on the classification of colour textures acquired by single-sensor colour cameras. In such cameras, the colour filter array (CFA) makes each photosensor sensitive to only one colour component, and CFA images must be demosaiced to estimate the final colour images. We show that demosaicing is detrimental to the textural information because it affects colour texture descriptors such as chromatic co-occurrence matrices (CCMs). However, it remains desirable to take advantage of the chromatic information for colour texture classification. This information is incompletely defined in CFA images, in which each pixel is associated to a single colour component. It is hence a challenge to extract standard colour texture descriptors from CFA images without demosaicing. We propose to form a pair of quarter-size colour images directly from CFA images without any estimation, then to compute the CCMs of these quarter-size images. This allows us to compare textures by means of their CCM-based similarity in texture classification or retrieval schemes, with still the ability to use different colour spaces. Experimental results achieved on benchmark colour texture databases show the effectiveness of the proposed approach for texture classification, and a complexity study highlights its computational efficiency.

Adaptive Matrices and Filters for Color Texture Classification

In this paper we introduce an integrative approach towards color texture classification and recognition using a supervised learning framework. Our approach is based on Generalized Learning Vector Quantization (GLVQ), extended by an adaptive distance measure, which is defined in the Fourier domain, and adaptive filter kernels based on Gabor filters. We evaluate the proposed technique on two sets of color texture images and compare results with those other methods achieve. The features and filter kernels learned by GLVQ improve classification accuracy and they are able to generalize much better for data previously unknown to the system.

Color texture analysis based on fractal descriptors

Color texture classification is an important step in image segmentation and recognition. The color information is especially important in textures of natural scenes, such as leaves surfaces, terrains models, etc. In this paper, we propose a novel approach based on the fractal dimension for color texture analysis. The proposed approach investigates the complexity in R, G and B color channels to characterize a texture sample. We also propose to study all channels in combination, taking into consideration the correlations between them. Both these approaches use the volumetric version of the Bouligand-Minkowski Fractal Dimension method. The results show a advantage of the proposed method over other color texture analysis methods.

Theoretical and experimental comparison of different approaches for color texture classification

Color texture classification has been an area of intensive research activity. From the very onset, approaches to combining color and texture have been the subject of much discussion, and in particular, whether they should be considered joint or separately. We present a comprehensive comparison of the most prominent approaches both from a theoretical and experimental standpoint. The main contributions of our work are: (i) the establishment of a generic and extensible framework to classify methods for color texture classification on a mathematical basis, and (ii) a theoretical and experimental comparison of the most salient existing methods. Starting from an extensive set of experiments based on the Outex dataset, we highlight those texture descriptors that provide good accuracy along with low dimensionality. The results suggest that separate color and texture processing is the best practice when one seeks for optimal compromise between accuracy and limited number of features. We believe that our work may serve as a guide for those who need to choose the appropriate method for a specific application, as well as a basis for the development of new methods.

Color Texture Classification Approach Based on Combination of Primitive Pattern Units and Statistical Features

Texture classification became one of the problems which has been paid much attention on by image processing scientists since late 80s. Consequently, since now many different methods have been proposed to solve this problem. In most of these methods the researchers attempted to describe and discriminate textures based on linear and non-linear patterns. The linear and non-linear patterns on any window are based on formation of Grain Components in a particular order. Grain component is a primitive unit of morphology that most meaningful information often appears in the form of occurrence of that. The approach which is proposed in this paper could analyze the texture based on its grain components and then by making grain components histogram and extracting statistical features from that would classify the textures. Finally, to increase the accuracy of classification, proposed approach is expanded to color images to utilize the ability of approach in analyzing each RGB channels, individually. Although, this approach is a general one and it could be used in different applications, the method has been tested on the stone texture and the results can prove the quality of approach.

Rotation invariant features for color texture classification and retrieval under varying illumination

This article proposes a new quaternion-based method for rotation invariant color texture classification under illumination variance with respect to direction and spectral band. The color of an object varies according to the spectral power distribution, object-illumination, and viewing geometry of the light source. The quaternion representation of color is shown to be effective, which treats color channels as single unit rather than separate components. New texture signatures are extracted by calculating the norm of the Quaternion fourier spectrum. These signatures are proved to be invariant under image rotation and illumination rotation. Moreover, these features are also invariant to the color spaces. The robustness of different color spaces against varying illumination in color Texture classification with 45 samples of 15 outex texture classes are examined. Comparative results show that the proposed method is efficient in rotation invariant texture classification.

Choice of a pertinent color space for color texture characterization using parametric spectral analysis

This article presents a comparison of different color spaces including RGB, IHLS and L⁎a*b* for color texture characterization. This comparison is based on the fusion of the independent spatial structure and color feature cues. In IHLS and L*a*b*, two channel complex color images are created from the luminance and the chrominance values. For such images, two dimensional complex multichannel linear prediction models are used to perform parametric power spectrum estimation and the structure feature cues are computed from this estimated power spectrum. Quantitative comparison of auto spectra of luminance and combined chrominance channels for different color spaces is done. This comparison is based on the degree of decorrelation between luminance and chrominance information provided by different color space transformations. Three dimensional histograms are used as color feature cues. Then, to classify color textures, Kullback–Leibler divergence based symmetric distance measures are calculated for pure color, luminance structure and chrominance structure feature cues. Individual as well as combined effect of information from all feature cues on classification results is then compared for different color spaces and different color texture data sets. The proposed color texture classification method performs better than the state of the art methods in certain cases. The L*a*b* color space gives us a better characterization of the chrominance spatial structure as well as the overall spatial structure for all of the chosen data sets. Experimental results on pixel classification of color textures are also presented and discussed.

Colour texture classification for Human Tissue Images

This paper presents a promising automotive technique for the classification of colour human tumour and nontumour tissues. The proposed classification algorithm is based on texture feature colour fuzzy texture spectrum and two more features from colour Doppler spectra. SOMs classify the tissues, which got a classification accuracy of 99.92%. Compared with other works, our method achieves good classification results on colour texture images, demonstrating the performance of our proposal.

Color texture classification across illumination changes

Color texture classification across illumination changes

Color spectral analysis for spatial structure characterization of textures in IHLS color space

In this article, a linear prediction model based approach for color texture characterization and classification in the improved hue luminance and saturation color space is presented. Pure chrominance structure information is used in addition to the normally used luminance structure information for color texture classification. Hue and saturation channels of a color image in IHLS color space are combined using a complex exponential to give a single channel which holds all the chrominance information of the image. Two dimensional complex multichannel versions of the non-symmetric half plane autoregressive model, the quarter plane autoregressive model and the Gauss Markov random field model are used to perform parametric power spectrum estimation of both luminance and the “combined chrominance” channels of the image. The accuracy and precision of these spectral estimates are proven quantitatively by performing tests on a large number of images. Spectral distance measures are calculated for the spectral information of luminance and chrominance channels individually as well as combined through a combination coefficient. Using these distance measures, color texture classification is done with k-nearest neighbor algorithm. Experimental results verify that the IHLS color space exhibits better performance than the RGB color space indicating the significance of using IHLS for such analysis. They also show that color texture characterization and percentage classification obtained by combined luminance and chrominance structure information is better than the color texture classification done using only the luminance structure information.

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.

A New Color-Texture Approach for Industrial Products Inspection

This work presents an approach for color-texture classification of industrial products. An extension of Gray Level Co-occurrence Matrix (GLCM) to color images is proposed. Statistical features are computed from an isotropic Color Co-occurrence Matrix for classification. The following color spaces are used: RGB, HSL and La*b*. New combination schemes for texture analysis are introduced. A comparison with Local Binary Patterns (LBP) is also performed. The tests were conducted in a variety of industrial samples. The obtained results are promising and show the possibility of efficiently classifying complex industrial products based on color and texture features.

On lexicographical ordering in multivariate mathematical morphology

Since mathematical morphology is based on complete lattice theory, a vector ordering method becomes indispensable for its extension to multivariate images. Among the several approaches developed with this purpose, lexicographical orderings are by far the most frequent, as they possess certain desirable theoretical properties. However, their main drawback consists of the excessive priority attributed to the first vector dimension. In this paper, the existing solutions to solving this problem are recalled and two new approaches are presented. First, a generalisation of α-modulus lexicographical ordering is introduced, making it possible to accommodate any quantisation function. Additionally, an input specific method is suggested, based on the use of a marker image. Comparative application results on colour noise reduction and texture classification are also provided.

Wavelet transform and adaptive neuro-fuzzy inference system for color texture classification

The wavelet domain features have been intensively used for texture classification and texture segmentation with encouraging results. More of the proposed multi resolution texture analysis methods are quite successful, but all the applications of the texture analysis so far are limited to gray scale images. This paper investigates the usage of Wavelet transform (WT) and Adaptive neuro-fuzzy inference system (ANFIS) for color texture classification problem. The proposed scheme composed of a wavelet domain feature extractor and an ANFIS classifier. Both entropy and energy features are used on wavelet domain. Different color spaces are considered in the experimental studies. The performed experimental studies show the effectiveness of the wavelet transform and ANFIS structure for color texture classification problem. The overall success rate is over 96%.

ICA-based multi-colour space texture classification system

Presented is a novel method which uses independent component analysis (ICA) for systematically partitioning and combining textural features extracted from different colour spaces, in a multiple classifier based system, for colour texture classification. Results obtained illustrate that the proposed ICA-based feature-partitioning and classifier combination system produces more accurate results compared to a system that combines classifiers applied to features extracted from individual colour spaces.

Color texture analysis using the wavelet-based hidden Markov model

The wavelet-domain hidden Markov model (WD HMM), in particular the hidden Markov tree (HMT), has recently been proposed and applied to gray texture analysis with encouraging results. For color texture analysis, the previous WD HMM can only be used to model the different color planes individually, assuming they are independent of each other. However, this assumption in general is unrealistic. We show in this paper that the wavelet coefficients have certain inter-dependences between color planes. This paper presents a novel approach to modeling the dependences between color planes as well as the interactions across scales. In our approach, the wavelet coefficients at the same location, scale and sub-band, but different color planes are grouped into one vector. We then propose a multivariate Gaussian mixture model (MGMM) for approximating the marginal distribution of the wavelet coefficient vectors in one scale and capturing the interactions of different color planes. In addition, the statistical dependence between different scales is captured by the transition matrix of the hidden Markov tree. Using this approach, we can improve the performance of the WD HMM on the color texture classification. The experiments show that our WD HMM approach provides 85% of correct classifications (PCC) on 68 color textures from Oulu texture database and outperforms the other wavelet-based methods we study. In this paper, we also investigated the classification performance of the WD HMM methods on different color spaces.

Texture classification system using colour space fusion

A novel colour texture classification system is presented based on an ensemble of independent classifiers each assigned to a different colour representation model. Gaussian Markov random fields features are used in a study to illustrate this previously unexplored approach and its potential to combine information from different colour spaces to improve accuracy. Experimental results illustrate that colour space information fusion results in significant improvements in accuracy.

Color texture classification by integrative Co-occurrence matrices

Integrative Co-occurrence matrices are introduced as novel features for color texture classification. The extended Co-occurrence notation allows the comparison between integrative and parallel color texture concepts. The information profit of the new matrices is shown quantitatively using the Kolmogorov distance and by extensive classification experiments on two datasets. Applying them to the RGB and the LUV color space the combined color and intensity textures are studied and the existence of intensity independent pure color patterns is demonstrated. The results are compared with two baselines: gray-scale texture analysis and color histogram analysis. The novel features improve the classification results up to 20% and 32% for the first and second baseline, respectively.

Histogram ratio features for color texture classification

This paper presents a color texture classification method using ratio features extracted from the color histogram. Combining pairs of bins and computing corresponding count ratios, ratio features are created that characterize the given color texture in an auto-correlative sense. The method is compared against the traditional color histogram method and improved results are obtained.