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In the field of image processing, texture features and color are fundamental visual cue with complementary roles. They are used in many applications and in a large variety of areas such as quality control, content-based image retrieval, remote sensing, industrial inspection, surface inspection, object recognition, and medical image analysis. For this purpose, a large number of algorithms have been proposed for texture feature extraction. Some of them are dedicated to gray-scale images while others aim at processing both color and texture. It has been shown that, for many cases, the use of color improves the performance of gray level texture classification. This paper provides a comprehensive survey of the texture feature extraction methods that consider both texture and color information. We propose a categorization of these methods into seven classes, two of them being very recent. For each method, we present the concept, the advantages and drawbacks, and we give examples of application.

Barhi is a high-quality date cultivar, typically harvested and consumed fresh at the Khalal maturity stage when the fruits are crispy and sweet with bright yellow color. The shelf-life of the Khalal stage (2-3 weeks) is very short for effective marketing. This study aimed at investigating the role of calcium chloride (CaCl2) and salicylic acid (SA) as pre-storage treatments in preserving the color and texture of the fruit during both cold and controlled atmosphere (CA) storage systems. Fresh-harvested Barhi fruits at the Khalal stage were treated with CaCl2 (2%) or SA (2.0 mM), and then stored in cold and CA systems. The weight loss and decay percentages in the treated samples decreased, whereas the total soluble solids of the treated samples increased compared with those of the untreated ones. The best treatment in limiting changes in the color and texture of the fruits was CaCl2 treatment during CA storage. [ABSTRACT FROM AUTHOR]

Purpose: Bronchoscopy is useful in lung cancer detection, but cannot be used to differentiate cancer types. A computer‐aided diagnosis (CAD) system was proposed to distinguish malignant cancer types to achieve objective diagnoses. Methods: Bronchoscopic images of 12 adenocarcinoma and 10 squamous cell carcinoma patients were collected. The images were transformed from a red–blue–green (RGB) to a hue–saturation–value (HSV) color space to obtain more meaningful color textures. By combining significant textural features (P < 0.05) in a machine learning classifier, a prediction model of malignant types was established. Results: The performance of the CAD system achieved an accuracy of 86% (19/22), a sensitivity of 90% (9/10), a specificity of 83% (10/12), a positive predictive value of 82% (9/11), and a negative predictive value of 91% (10/11) in distinguishing lung cancer types. The area under the receiver operating characteristic curve was 0.82. Conclusions: On the basis of extracted HSV textures of bronchoscopic images, the CAD system can provide recommendations for clinical diagnoses of lung cancer types. [ABSTRACT FROM AUTHOR]

Current color texture feature extraction methods transform color images into gray images or process color image by channel separation, which can lead to color information loss of the original image or correlation loss between channels, so that the texture feature of the feature image differs greatly from that of the original image. To solve the above mentioned problems, we propose a quaternion Gabor method to extract color texture features. Firstly, quaternion Gabor filter is deduced according to traditional Gabor filter and quaternion Euler's formula, and the color image is described by Quaternion field. Secondly, we propose a quaternion Gabor convolution algorithm to process the color image, and obtain a multi-scale, multi-direction color texture image. Finally, Tamura statistical feature is extracted from the color texture image. Experimental results show that the proposed method can maintain text features of the original image such as coarseness, contrast and directionality to a great extent and meanwhile obtain color information, which outperforms the traditional Gabor feature image and LBP method. [ABSTRACT FROM AUTHOR]

Background and objectives Lung cancer is the leading cause of cancer deaths worldwide. With current use of autofluorescent bronchoscopic imaging to detect early lung cancer and limitations of pathologic examinations, a computer-aided diagnosis (CAD) system based on autofluorescent bronchoscopy was proposed to distinguish different pathological cancer types to achieve objective and consistent diagnoses. Methods The collected database consisted of 12 adenocarcinomas and 11 squamous cell carcinomas. The corresponding autofluorescent bronchoscopic images were first transformed to a hue (H), saturation (S), and value (V) color space to obtain better interpretation of the color information. Color textural features were respectively extracted from the H, S, and V channels and combined in a logistic regression classifier to classify malignant types by machine learning. Results After feature selection, the proposed CAD system achieved an accuracy of 83% (19/23), a sensitivity of 73% (8/11), a specificity of 92% (11/12), a positive predictive value of 89% (8/9), a negative predictive value of 79% (11/14), and an area under the receiver operating characteristic curve of 0.81 for distinguishing lung cancer types. Conclusions The proposed CAD system based on color textures of autofluorescent bronchoscopic images provides a diagnostic method of malignant types in clinical use. [ABSTRACT FROM AUTHOR]

We present color image processing methods for the char\-ac\-te\-ri\-za\-tion of images of dermatological lesions for the purpose of content-based image retrieval (CBIR) and computer-aided di\-ag\-no\-sis. The intended application is to segment the images and perform classification and analysis of the tissue composition of skin lesions or ulcers, in terms of granulation (red), fibrin (yel\-low), necrotic (black), callous (white), and mixed tissue composition. The images were analyzed and classified by an expert dermatologist following the red-yellow-black-white model. Automatic segmentation was performed by means of clustering using Gauss\-ian mixture modeling, and its performance was evaluated by deriving the Jaccard coefficient between the automatically and manually segmented images. Statistical texture features were derived from cooccurrence matrices of RGB, HSI, L$^*$a$^*$b$^*$, and L$^*$u$^*$v$^*$ color components. A retrieval engine was implemented using the k-nearest-neighbor classifier and the Euclidean, Man\-hat\-tan, and Chebyshev distance metrics. Classification was performed by means of a metaclassifier using logistic regression. The average Jaccard coefficient after the segmentation step between the automatically and manually segmented images was 0.560, with a standard deviation of 0.220. The performance in CBIR was mea\-sured in terms of precision of retrieval, with average values of up to 0.617 obtained with the Chebyshev distance. The metaclassifier yielded an average area under the receiver operating char\-ac\-ter\-is\-tic curve of 0.772.

This paper presents a color-texture descriptor based on the local mapped pattern approach for color-texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color-texture information from the images. These features are combined with texture information from the luminance image in a multiresolution fashion to get the ECLMP feature vector. The robustness of the proposed method is evaluated using the RawFooT, KTH-TIPS-2b, and USPtex databases. The experimental results show that the proposed descriptor is more robust to changes in the illumination condition than 22 alternative commonly used descriptors. [ABSTRACT FROM AUTHOR]

Texture classification plays a major role in many computer vision applications. Local binary patterns (LBP) encoding schemes have largely been proven to be very effective for this task. Improved LBP (ILBP) are conceptually simple, easy to implement, and highly effective LBP variants based on a point-to-average thresholding scheme instead of a point-to-point one. We propose the use of this encoding scheme for extracting intra- and interchannel features for color texture classification. We experimentally evaluated the resulting improved opponent color LBP alone and in concatenation with the ILBP of the local color contrast map on a set of image classification tasks over 9 datasets of generic color textures and 11 datasets of biomedical textures. The proposed approach outperformed other grayscale and color LBP variants in nearly all the datasets considered and proved competitive even against image features from last generation convolutional neural networks, particularly for the classification of biomedical images. [ABSTRACT FROM AUTHOR]

Color texture classification as a part of fabric analysis is significant for textile manufacturing. In this research, a new artificial intelligence method based on a dual-side co-occurrence matrix and a back propagation neural network has been proposed for color texture classification, which could achieve relatively accurate classification results for yarn-dyed woven fabric compared with the traditional co-occurrence matrix for a single-side image. Firstly, a laboratory dual-side imaging system has been established to digitize the upper-side and lower-side images sequentially. Secondly, the dual-side co-occurrence matrix could be generated based on these dual images; four texture features could be extracted for the evaluation of the fabric texture characteristics. Thirdly, a well-trained back propagation neural network was established with the four defined features as the input vectors and the color texture type of yarn-dyed woven fabric as the output vector. The efficiency of two different classification systems based on a dual-side co-occurrence matrix and a single-side co-occurrence matrix has been compared systematically. Our experimental results show that the artificial intelligence system based on a dual-side co-occurrence matrix and back propagation neural network model could achieve a relatively better classification effect, with the high coefficient ratio (R = 0.9726) when d = 0. [ABSTRACT FROM AUTHOR]

Aiming to identify plants, we propose to evaluate the color texture of the palisade parenchyma, from microscopic images of leaf cross-sections, using a graph based approach. Our texture analysis approach models the image texture as a graph and uses measurements computed from the shortest paths between specific vertices to provide a feasible texture signature. For a more consistent evaluation, we compared our approach to different methods for color texture analysis in a texture classification experiment. The results obtained indicate that our approach is the most suitable for this histological analysis as it surpassed all the other texture approaches using Linear Discriminant Analysis, and obtained the second best accuracy using 1-Nearest Neighbor. These results also corroborate the feasibility of using both histological (as the palisade parenchyma) and computer analysis for identification and delimitation of plant taxa. [ABSTRACT FROM AUTHOR]

The color and texture are very important features in image analysis. In this paper, the combination of these features is presented. Several approaches for color texture features extraction are researched. The improvement of the one-dimensional version of GLCM (Gray Level Co-occurrence Matrix) for color textures was designed. We named this method as Color Level Co-occurrence Matrix (1D-CLCM). The experiments on database of 2600 color images are provided. Finally, the evaluation and comparison of image retrieval results for separate methods are presented.

These last few years, several supervised scores have been proposed in the literature to select histograms. Applied to color texture classification problems, these scores have improved the accuracy by selecting the most discriminant histograms among a set of available ones computed from a color image. In this paper, two new scores are proposed to select histograms: The adapted Variance score and the adapted Laplacian score. These new scores are computed without considering the class label of the images, contrary to what is done until now. Experiments, achieved on OuTex, USPTex, and BarkTex sets, show that these unsupervised scores give as good results as the supervised ones for LBP histogram selection.

the content based image retrieval is an important research matter since people are using and transferring more and more video and image data. Earlier the research was confined to searching and downloading the image from the cyberspace. Now-a-days, owing to syntax attributes and ability of compression of vast data, CBIR techniques are proving for useful. It has become feasible to locate the desired image with the aid of their properties and tags. The recent advancements and developments in the various techniques and process, for increasing the accuracy in finding the required image is being discussed, in the paper. [ABSTRACT FROM AUTHOR]

A number of texture classification approaches have been developed in the past but most of these studies target graylevel textures. In this work, novel results are presented on Neural Network based classification of color textures in a very large heterogeneous database. Several different Multispectral Random Field models are used to characterize the textures. The classifying features are based on the estimated parameters of these model and functions defined on them. The approach is tested on a database of 73 different color textures classes. The advantage of utilizing color information is demonstrated by converting color textures to gray-level ones and classifying them using Grey Level Co-Occurrence Matrix (GLCM) based features.

This paper describes a color texture-based image segmentation system. The color texture information is obtained via modeling with the Multispectral Simultaneous Auto Regressive (MSAR) random field model. The general color content characterized by ratios of sample color means is also used. The image is segmented into regions of uniform color texture using an unsupervised histogram clustering approach that utilizes the combination of MSAR and color features. The performance of the system is tested on two databases containing synthetic mosaics of natural textures and natural scenes, respectively