Search

Identifying locations of protein expression in live cells plays an important role in several medical applications ranging from early disease diagnosis to monitoring effectiveness of drugs. Protein localization is directly related to their cell types. Today florescence imaging is widely used to understand biology at the cellular level. Hence, cell phenotype classification in fluorescence microscope images, is related with protein localization. Today it is performed by human, which is very time consuming with low accuracy. According to the visual structure, it can be seen that samples of a unique cell type have quite similar texture, but the texture of different cell types, are very different. In this respect, texture information can be used more widely than shape or color information, to classify types. Local ternary pattern is a noise-resistant texture descriptor that provides discriminative features. In this paper a local texture analysis descriptor is proposed titled multi threshold uniform-based local ternary patterns with notation MT-ULTP. MT-ULTP extracts local significant texture information in different locality levels. In this respect, local ternary patterns are extracted in different thresholds and finally the occurrence probability of the uniform patterns is extracted as features. MT-ULTP is a skillful combination of LTP and MLBP with novelty in feature extracting and local pattern selecting. Performance of the proposed descriptor is evaluated on 2d-hela dataset in terms of accuracy. 2d-hela is the benchmark dataset of cell phenotype images. Experimental results show that MT-ULTP provides higher classification rates than very well-known texture descriptors such as lbp-like descriptors. In other experiments, it has been shown that ignoring uniform textural patterns in the image analysis can increase the accuracy of cell phenotype classification and some other computer vision-based applications. The results also showed that extraction uniform patterns based on a combination of thresholds provide better results than the simple form in local ternary patterns. The proposed image texture descriptor is a general case which can be used in many computer vision applications to describe the image contents.

Automatic classification in medical image processing with new problems to computer-aided detection methods is attributed to the dynamics of the image acquisition conditions. Such problems request t...

Content-based image retrieval (CBIR) is an image retrieval technique that can retrieve images by matching its feature set values. This research focuses on a novel CBIR method namely hybrid CBIR method using statistical, Discrete Wavelet Transform (DWT)-Entropy and Peak-oriented Octal Pattern-derived Majority Voting (POPMV)-based feature sets (CBIR_SWPOPMV) to efficiently extract the relevant colour images from the colour image dataset. The doctrine of the proposed method is influenced by a novel texture descriptor namely POPMV which is an octal pattern based on the histogram peak information, to bring about a majority voting-based feature set and three histogram-based feature sets. Furthermore, to improve the retrieval accuracy, the DWT-based Entropy feature set and the statistical feature set are also included. Finally, the Euclidean distance-based matching process brings more favourable relevant images with respect to the query image. The proposed methodology is experimentally compared with the existing recent CBIR versions by using seven standard databases such as Corel-1k, USPTex, MIT-VisTex, KTH-TIPS, KTH-TIPS2a, KTH-TIPS2a, Colored Brodatz and a user-contributed database named DB_VEG.

Lung cancer is one of the leading causes of death in the world. Although early detection of lung tumours (nodules) can remarkably diminish the mortal rate, precise detection of them is not always possible by visual inspection of the computerised tomography images. Since nodules with different sizes have non-uniform shape and brightness, texture attributes and also the gradient of orientation can be good candidate features, which have been used for this purpose. They determined the co-occurrence matrix of the extended local binary pattern (ELBP) along with weighted orientation difference (WOD) for each sub-region of the lung area. Local binary pattern is a texture descriptor that can extract the discriminative features efficiently. The proposed ELBP is rotation invariant and suitable to describe non-uniform patterns. Moreover, WOD as a structural feature uses the magnitude of each edge as the weight of its orientation difference. After constructing the co-occurrence matrix, discriminative features were extracted from this matrix and fed into a support vector machine in order to classify each sub-region as a cancerous (nodule) or normal tissue. The proposed method was compared to some of state-of-the-art nodule detection methods and was assessed over several real datasets in terms of specificity, sensitivity and accuracy.

Dynamic texture (DT) is the annexation of an altering series of images where temporal regularity is present. Characterization, classification and identification of DTs have been investigated by many researchers in the past 2 decades. Especially, several image texture descriptors based on local binary pattern (LBP) have been enhanced for DT classification in the spatiotemporal domain. Local tetra pattern (LTrP) is an extension of LBP in which the calculation of the feature code depends on the referenced central pixel as well as its neighbor’s directions. In this work, we have proposed an LTrP-based novel spatiotemporal descriptor for characterization and subsequent identification of textures in videos. We term this descriptor as local tetra patterns on three orthogonal planes (LTrP-TOP). In our proposed work, the video frames are initially divided into three orthogonal planes on the basis of the XY, YT and XT directions. The direction of the center and neighborhood pixels are then detected in vertical and horizontal orientations using first-order derivatives. This sequence of operations computes the LTrP for each center pixel. Subsequently, we create the histogram for a particular plane by considering the LTrP codes for all the pixels in that plane. Finally, the histograms from the three orthogonal planes are concatenated for forming the final texture descriptor of the video. Comprehensive experimental assessments on benchmark DT databases (Dyntex++ and UCLA) demonstrate that the LTrP-TOP descriptor exhibits better performance than most of the other state-of-the-art DT descriptors (in terms of classification accuracy %).

In case of illumination change, the local binary pattern (LBP) descriptor have found to be used in analysis of texture of the image because of the ease of computation and robustness to such changes. However, the LBP technique also comes with limitations such as its inability to capture the discriminative information completely. For enhancing the LBP’s performance, we proposed a new texture descriptor for rotation, illumination and scale invariance (IRSLBP) for texture classification. The proposed approach extracts the color features through quantification of the RGB space into single channel, which is marked by a smaller number of shades to reduce computation and to improve the efficiency. The IRSLBP descriptor provides the scale invariance by considering the circular neighbor set of every central pixel other than the normal neighboring pixels. Moreover, the proposed IRSLBP decomposed the difference vector into sign part and magnitude part by local difference sign magnitude transform. In addition, these mitigated the influence of rotation, illumination or noise and demonstrated effective robustness. Using the proposed IRSLBP descriptor, we have classified the different textures using Multi kernel support vector machine (SVM) approach.

Finger Texture (FT) images acquired from different spectral lighting sensors reveal various features. This inspires the idea of establishing a recognition model between FT features collected using two different spectral lighting forms to provide high recognition performance. This can be implemented by establishing an efficient feature extraction and effective classifier, which can be applied to different FT patterns. So, an effective feature extraction method called the Surrounded Patterns Code (SPC) is adopted. This method can collect the surrounded patterns around the main FT features. It is believed that these patterns are robust and valuable. The SPC approach proposes using a single texture descriptor for FT images captured under multispectral illuminations, where this reduces the cost of employing different feature extraction methods for different spectral FT images. Furthermore, a novel classifier termed the Re-enforced Probabilistic Neural Network (RPNN) is proposed. It enhances the capability of the standard Probabilistic Neural Network (PNN) and provides better recognition performance. Two types of FT images from the Multi-Spectral CASIA (MSCASIA) database were employed as two types of spectral sensors were used in the acquiring device: the White (WHT) light and spectral 460 nm of Blue (BLU) light. Supporting comparisons were performed, analysed and discussed. The best results were recorded for the SPC by enhancing the Equal Error Rates (EERs) at 4% for spectral BLU and 2% for spectral WHT. These percentages have been reduced to 0% after utilizing the RPNN.

Minutia Cylinder Codes (MCC) are minutiae based fingerprint descriptors that take into account minutiae information in a fingerprint image for fingerprint matching. In this paper, we present a modification to the underlying information of the MCC descriptor and show that using different features, the accuracy of matching is highly affected by such changes. MCC originally being a minutia only descriptor is transformed into a texture descriptor. The transformation is from minutiae angular information to orientation, frequency and energy information using Short Time Fourier Transform (STFT) analysis. The minutia cylinder codes are converted to minutiae texture cylinder codes (MTCC). Based on a fixed set of parameters, the proposed changes to MCC show improved performance on FVC 2002 and 2004 data sets and surpass the traditional MCC performance.

Forgery detection from the images is gaining remarkable interest as there are a lot of editing tools that enable to cause edition with manipulation or removal of the objects from the images. This study proposes a new forgery detection scheme that is based on the supervised learning approach. The supervised learning is brought about by using the support vector neural network and the optimisation is enabled using the fruit fly optimisation algorithm. Initially, the images are fed to the texture descriptor and the face is detected using the Viola-Jones algorithm. The face detected images are subjected to the feature extraction using the Gabor filter + wavelet + texture operator and the features are concatenated to present the input to the classifier. Then, the classifier which is trained using the fruit fly optimisation classifies the features to detect the presence of the manipulation. The performance of the proposed scheme is evaluated with the existing methods for the evaluation metrics accuracy, sensitivity, and specificity using two datasets, namely DSO-1 and DSI-1. The analysis shows that the proposed scheme attained an accuracy of 0.9523, the sensitivity of 0.94, and the specificity of 0.9583, which are greater when compared to the existing methods.

Texture classification is one of the recently popular study topics in pattern recognition. Local binary pattern (LBP) is a very efficient local texture descriptor and is used for feature extraction in texture recognition. There are five main steps in representation of texture images: neighbourhood topology and sampling, thresholding and quantisation, encoding and regrouping, combining complementary features. In this study, the authors used symmetric two spirals LBP to measure the grey-scale difference between the centre pixel and its neighbours. They also extended the proposed method by using four spirals LBP to generate the LBP code. For classification, linear regression classification method, which is generally used to solve the face recognition problems, is used. The authors tested the performance of their method on UIUC and CUReT texture image databases. It is experimentally demonstrated that the proposed method achieves the highest classification accuracy among the comparative methods on texture databases.

Searching for similar images in large databases is a time-consuming work and an efficient image retrieval system is valuable in this situation. Texture is a prominent feature of image which can extract contents of image, hence a good texture feature extractor is necessary for content based image retrieval process. In this paper, a new texture descriptor is developed which is a combination of Local Ternary Pattern (LTP) and gray level co-occurrence matrix (GLCM). This feature descriptor which is named as CoALTP, inherits the attributes of both LTP and GLCM. First LTPs of pixels are obtained and then using GLCM in four directions, co-relations between pixel pairs are calculated as features. Two texture databases and a face images dataset are used for evaluation of proposed descriptor and results are compared with other local feature descriptors.

Textures widely exist in the natural scenes while traditional level set models generally use only intensity information to construct energy and ignore the inherent texture features. Thus these models have difficulty in segmenting texture images especially when the texture objects have similar intensity to the background. To solve this problem, we propose a new level set model for texture segmentation that considers the impact of local Gaussian distribution fitting (LGDF), local self-similarity (LSS) and a new numerical scheme on the evolving contour. The proposed method first introduces a texture energy term based on the local self-similarity texture descriptor to the LGDF model, and then the evolving contour could effectively snap to the textures boundary. Secondly, a lattice Boltzmann method (LBM) is deployed as a new numerical scheme to solve the level set equation, which can break the restriction of the Courant–Friedrichs–Lewy (CFL) condition that limits the time step of iterations in former numerical schemes. Moreover, GPU acceleration further improves the efficiency of the contour evolution. Experimental results show that our model can effectively handle the segmentation of synthetic and natural texture images with heavy noises, intensity inhomogeneity and messy background. At the same time, the proposed model has a relatively low complexity.

Texture descriptors such as local binary patterns (LBP) have been successfully employed for feature extraction in image retrieval algorithms because of their high discriminating ability and computational efficiency. In this paper, we propose histogram feature refinement methods for enhancing performance of texture descriptor based content-based image retrieval (CBIR) systems. In the proposed approach for histogram refinement, each pixel in the query and database images is classified into one of the two categories based on the analysis of pixel values in its neighborhood. Local patterns corresponding to two sets of pixels are used to generate two histogram features for each image, effectively resulting in splitting of the original global histogram of texture descriptors into two based on the category of each pixel. Resulting histograms are then concatenated to form a single histogram feature. This study also explores three hybrid frameworks for histogram refinement in CBIR systems. Comparison of histogram features corresponding to query and database images are performed using the relative l1 distance metric. Performance evaluation on three publicly available benchmark image databases namely, GHIM 10000, COREL 1000 database, and Brodatz texture database shows that performances of existing texture descriptor based approaches improve considerably when the proposed histogram feature refinement is incorporated. Specifically, the average precision rate is improved by 6.02%, 5.69%, 4.79%, and 4.21% for LBP, local derivative pattern (LDP), local ternary pattern (LTP), and local tetra pattern (LTrP) descriptors, respectively on GHIM 10000 database. The proposed histogram refinement approaches also provide performance improvement for other texture descriptors considered in this study. A general framework for histogram refinement of texture descriptors.Improves retrieval performance of texture descriptor based CBIR systems.Performance improvement in CBIR validated using 9 texture descriptors.Marginal increase in average image retrieval time with 1.2s in the worst case.

Local binary pattern (LBP) is a frequently-used texture descriptor. Lots of LBP-variants have been proposed to improve its performance of representing textures. However, most of them ignore the global and neighbour-difference information of an image texture. In this study, the authors propose a structural difference histogram representation by fusing the segmented structure pattern (SSP), the refined LBP (RLBP) and the neighbour-difference pattern (NDP) for texture classification. Particularly, the segmented structure, which contains the global contour information of an image texture, is first constructed to compute its SSP histogram feature. Simultaneously, the RLBP is defined to represent the local texture information. Furthermore, the NDP is presented to describe differences between neighbours of a centre pixel in the local patch of texture images. Experimental results on Brodatz and Columbia-Utrecht reflectance databases indicate that the proposed method can achieve the satisfactory classification accuracy compared with several representative methods.

A texture descriptor based on the shape index and the accompanying curvedness measure is proposed, and it is evaluated for the automated analysis of astronomical image data. A representative sample of images of low-red shift galaxies from the Sloan Digital Sky Survey (SDSS) serves as a test bed. The goal of applying texture descriptors to these data is to extract novel information about galaxies, information which is often lost in more traditional analysis. In this study, we build a regression model for predicting a spectroscopic quantity, the specific star-formation rate (sSFR). As texture features we consider multi-scale gradient orientation histograms as well as multi-scale shape index histograms, which lead to a new descriptor. Our results show that we can successfully predict spectroscopic quantities from the texture in optical multi-band images. We successfully recover the observed bi-modal distribution of galaxies into quiescent and star-forming. The state-of-the-art for predicting the sSFR is a color-based physical model. We significantly improve its accuracy by augmenting the model with texture information. This study is the first step towards enabling the quantification of physical galaxy properties from imaging data alone.

Weber’s law for image feature descriptor (WLD) is based on the theory that the ratio of the increment threshold to the background intensity is a constant. It has been used in facial recognition, structure detection, and tissue classification in X-ray images. In this paper, WLD is explored in the polyp classification in color colonoscopy images for the first time. An open, on-line colonoscopy image database is used to evaluate the new descriptor. The database contains 74 polyps, including 19 benign polyps and 55 malignant ones. Each polyp has a white light image (WLI) and a narrow band image (NBI), both were obtained by the same fibro-colonoscopy from the same patient. WLD image texture features are extracted from three color channels of (1) color WLI, (2) color NBI and (3) WLI+NBI. The extracted features are analyzed, ranked and classified using a Random Forest package based on the merit of the area under the curve (AUC) of the Receiver Operating Characteristics (ROC). The performance of WLD is quantitatively documented by the AUC, the ROC curve, the P-R (precision-recall) plot and the accuracy measure with comparison to commonly used features, such as Haralick and local binary pattern feature descriptors. The results demonstrate the advantage of WLD in the polyp classification in terms of the quantitative measures.

A texture recognition method is proposed.The image is modeled into networks.The texture is characterized by the diffusion over the networks.It is demonstrated that directed networks are better to texture recognition.The proposed method outperform the state-of-the-art. Much work has been done in the field of texture analysis and classification. While promising classification methods have been proposed, most of them rely on classical image analysis approaches. This paper presents a texture classification method based on diffusion in directed networks. First, an image is modeled as a directed network by mapping each pixel as a node and connecting two nodes up to a maximum distance r. To reveal texture properties, links between two nodes are removed based on the pixel intensity difference. Once such a network is obtained, the activity of each node is estimated by random walks and combined into a histogram to describe the image. The main contribution of this paper is the use of directed networks, which tends to provide better performance than in undirected cases. Also, we have shown that the activity induced on these networks can be effectively used as texture descriptor. Experimental results show that the proposed method is favorably compared to traditional texture methods on widely used texture datasets. The proposed method is also found to be promising for plant species classification using samples of leaf texture.

In this paper, a novel algorithm for textural feature description in very high resolution (VHR) satellite imagery is developed. It is based on a pointwise (PW) approach on the feature covariance matrix. The main motivation of this work is to construct the covariance matrix of oriented gradients (COG) using a nondense approach based on characteristic points (i.e., keypoints) extracted from the image. The proposed descriptor, which is named PW-COG, is expected to be effective when applied to VHR images. First, a COG-based descriptor is capable of not only capturing both radiometric and local geometric information (given by gradient features) from the image but also encoding their joint distribution and correlation, which are effectively relevant for texture characterization and discrimination. Second, by employing a keypoint-based approach, the proposed method is able to deal with large amount of VHR image data, since we do not take into consideration all pixels of the image, without requiring the stationarity hypothesis. In order to demonstrate the effectiveness of the proposed descriptor, texture-based image classification is carried out. Experimental study on both Brodatz texture database and VHR satellite images using the proposed algorithm provides very competitive results, in terms of texture discrimination and algorithm complexity, compared to reference methods.

Color, texture, shape and luminance are the prominent features for image segmentation. Texture is an organized group of spatial repetitive arrangements in an image and it is a vital attribute in many image processing and computer vision applications. The objective of this work is to segment the texture sub images from the given arbitrary image. The main contribution of this work is to introduce “NEW” texture feature descriptor to the image segmentation field. The NEW texture descriptor labels the neighborhood pixels of a pixel in an image as N,W,NW,NE,WW,NN and NNE(N-North, W-West).To find the prediction value, the gradient of the intensity functions are calculated. Eight component binary vectors are formed and compared to prediction value. Finally end up with 256 possible vectors. Fuzzy c-means clustering is used to segment the similar regions in textural image Extensive experimentation shows that the proposed methodology works better for segmenting the texture images , and also segmentation performance are evaluated.

Plastic surgery is considered as a challenging research issue in the field of face recognition. Nevertheless, it has yet to be studied from theoretical and experimental perspectives. In this study, the authors proposed a facial recognition system for recognising faces after plastic surgery, which fuses the scores of two feature-based and texture-based algorithms. The feature based algorithm is the image GIST global descriptor and the texture-based algorithm is the local binary pattern (LBP) of silence points. First, the local texture descriptor LBP was applied over a set of key points (silence points) in the face image rather than applying it over the entire face area. This proposed feature set is based on the assumption that only those LBP patterns with certain meaning, such as an edge or corner, will be useful for recognising faces that have undergone plastic surgery. The second set of features was extracted using a global descriptor, which is the GIST descriptor, to obtain a basic and a subordinate level description of the perceptual dimension. The performance of the proposed system surpassed the performance of a number of state-of-the-art face recognition after plastic surgery, with a maximum verification accuracy of more than 91%.

A good image representation is critical to face recognition task. Recently, eight-direction Kirsch masks based image descriptors, e.g. local directional pattern (LDP), local sign directional pattern (LSDP), have been devised and shown competitive results than conventional LBP descriptor. However, these methods may lose or do not fully explore valuable texture information of the image. To remedy this drawback, a novel discriminative image descriptor, namely local edge direction and texture descriptor (LEDTD) is proposed in this paper. LEDTD differs from the existing Kirsch based methods in a manner that it not only considers image edge direction information but also extracts image texture feature by encoding the edge response directions of center and its neighborhood pixels by employing local XOR binary coding strategy. Finally, edge direction and texture features are integrated to form the image feature vector. Extensive performance evaluations on four benchmark face databases show that the proposed approach yields a better performance in terms of the recognition rate as well as robustness to the noise compared with the state of the art methods.

Texture feature extraction methods have been improved greatly in recent years. It is widely known that the local texture feature descriptor can achieve desired performance under the change of image geometric size, different poses and complex illumination conditions. In this paper, a novel local texture descriptor is proposed, named as the multi-degrees improved local difference binary (ILDB). Local difference binary is an promising feature description method, while it only computes the intensity and gradient difference on pairwise grid cells and ignores the image grid inherent texture gradient difference. ILDB can represent difference and texture information of the grid cells intensity and gradient simultaneously. In addition, the multiple-degree strategy is adopted to achieve richer texture description. At the same time, the optimized mutual information is proposed to capture more discriminant feature selection and reduce the dimensionality of the ILDB. Experimental results demonstrate that the proposed method is highly efficient and distinctive compared with several state-of-the-art approaches. Due to good performance of ILDB, it is expected that ILDB has a potential for widespread application in many computer vision fields.

International audience; The proposal of this study is to analyze the texture pattern recognition over the local jet space looking forward to improve the texture characterization. Local jets decompose the image based on partial derivatives allowing the texture feature extraction be exploited in different levels of geometrical structures. Each local jet component evidences a different local pattern, such as, flat regions, directional variations and concavity or convexity. Subsequently , a texture descriptor is used to extract features from 0th, 1st and 2nd-derivative components. Four well-known databases (Brodatz, Vistex, Usptex and Outex) and four texture descriptors (Fourier descriptors, Gabor filters, Local Binary Pattern and Local Binary Pattern Variance) were used to validate the idea, showing in most cases an increase of the success rates.

A texture descriptor is a collection of quantified measurements of a texture's properties. They are used to describe how the basic unit of a texture is repeated in an image, such as the pattern size, pattern linelikeness, directional unification and directionality. These quantified measures have often been calculated in the spatial domain. This paper proposes a new approach of calculating texture deature descriptors in the frequency domain. It also introduces new texture feature descriptors, which describe how the unit of texture is repeated global throughout the image, such as pattern formation linelikeness and repetition directionality.

Texture classification is a problem widely studied in computer vision, there exist two fundamental issues: how to describe texture images and how to define a similarity measure. The texture descriptors are mainly used to extract and represent the features of texture images and their performance is usually measured using a classification algorithm. In this paper, some of the most referenced texture descriptors, such as Gabor filter banks, Wavelets, and Local Binary Patterns, are compared using non-parametric statistical tests to know if there is a difference in performance. The descriptors are applied to five well-known texture image datasets, in order to be classified. Three classification algorithms, with a cross-validation scheme, are used to classify the described texture datasets. Finally, a Friedman test with multiple comparisons is used to compare the whole performance of the texture descriptors on a statistical basis. The statistical results suggest that for these tests there is a difference in performance, so it was possible to determine statistically, for the considered experimental settings, the best texture descriptor.

This letter addresses the texture classification problem through a pixel-based local binary pattern (LBP) statistics aggregation mechanism. Real-world texture images often present challenges for classification algorithms in terms of intra-class variability due, among others, to variable illumination. The LBP operator, a state-of-the-art texture descriptor, possesses key properties for tackling real-world texture images: discriminative power and invariance against monotonic gray level changes. We propose a novel texture classification approach that increases the robustness of LBP-based methods with respect to any type of intra-class variations. The method locally characterizes each pixel with an LBP code histogram and globally computes the label of a textured image by aggregating pixel labels through a voting process. Our approach can be in principle applied to any LBP version, as it focuses on how statistics are computed from LBP codes. We show that the proposed pixel-based approach improves upon traditional LBP block-based approaches in terms of classification accuracy by up to 5.1 p.p. on the public Outex database for the classic LBP with various neighborhoods as well as for various LBP extensions.

In this letter, we present a rotation-invariant texture descriptor in a multi-resolution framework.The proposed method is robust to Gaussian noise, and it can be used for both noise-free and noisy texture classification.Experimental results on three well-known texture datasets indicate that the proposed method achieves high texture classification accuracy, especially in the presence of various levels of Gaussian noise. In this letter an effective multi-resolution and rotation-invariant texture description approach is presented, which can be utilized in various computer vision and image processing tasks such as texture classification and texture segmentation. In the proposed method, a given gray-scale texture image is first processed by a bank of Gabor wavelets (filters) at different scales and orientations. The obtained filters' responses are then further processed, and a set of local binary patterns called "Local Gabor Wavelets Binary Patterns" (LGWBPs) are computed by comparing the local filters' outputs at different orientations with the global mean of filters' outputs at the same orientations. The obtained patterns are then converted to a number of decimal rotation-invariant codes, and a histogram of the resultant codes at different scales is finally used as a texture feature vector. Experimental results on three popular texture datasets (Outex TC10, CUReT, and Brodatz) indicate that the proposed method achieves high texture classification accuracy, especially in the presence of various levels of Gaussian noise.

Material recognition has several applications, such as image retrieval, object recognition and robotic manipulation. To make the material classification more suitable for real-world applications, it is fundamental to satisfy two characteristics: robustness to scale and to pose variations. In this study, the authors propose a novel discriminant descriptor for texture classification based on a new operator called local combination adaptive ternary pattern (LCATP) descriptor used to encode both colour and local information. They start by building the LCATP descriptor using a combination of three different adaptive thresholding techniques. Moreover, they present a novel operator, mean histogram (MH), used jointly with the LCATP in order to incorporate colour information into the descriptor. This approach is then extended to four different colour spaces: LC 1 C 2, I 1 I 2 I 3, LSHuv and O 1 O 2 O 3. The final descriptor, LCATP fusion (LCATP_F), is produced by fusing the basic histogram (H) and MH extracted from the different colour spaces. Finally, the LCATP_F descriptor properties, such as the robustness to scale and pose changes are evaluated using the challenging KTH-textures under varying illumination, pose and scale (TIPS2b) dataset along with the least squares support vector machines classifier. The obtained experimental results, using the LCATP_F descriptor, show a significant improvement with respect to the state-of-the-art results.

Because texture images cannot be directly processed by the gray level information of individual pixel, we propose a new texture descriptor which reflects the intensity distribution of the patch centered at each pixel. Then the general multiphase image segmentation model of Potts model is extended for texture segmentation by adding the region information of the texture descriptor. A fast numerical scheme based on the split Bregman method is designed to speed up the computational process. The algorithm is efficient, and both the texture descriptor and the characteristic functions can be implemented easily. Experiments using synthetic texture images, real natural scene images and synthetic aperture radar images are presented to give qualitative comparisons between our method and other state-of-the-art techniques. The results show that our method can accurately segment object regions and is competitive compared with other methods especially in segmenting natural images.

We show that combining multiple texture description methods significantly improves the performance compared to using the single best texture method alone.We further propose to use information theoretic compression approach to compress high-dimensional multi-texture features into a compact heterogeneous texture representation.We perform a comprehensive evaluation of color features, popular in object recognition, for the task of texture classification.We show that late fusion of our compact texture descriptor with discriminative color feature outperforms state-of-the-art results on challenging texture recognition datasets. Describing textures is a challenging problem in computer vision and pattern recognition. The classification problem involves assigning a category label to the texture class it belongs to. Several factors such as variations in scale, illumination and viewpoint make the problem of texture description extremely challenging. A variety of histogram based texture representations exists in literature. However, combining multiple texture descriptors and assessing their complementarity is still an open research problem. In this paper, we first show that combining multiple local texture descriptors significantly improves the recognition performance compared to using a single best method alone. This gain in performance is achieved at the cost of high-dimensional final image representation. To counter this problem, we propose to use an information-theoretic compression technique to obtain a compact texture description without any significant loss in accuracy. In addition, we perform a comprehensive evaluation of pure color descriptors, popular in object recognition, for the problem of texture classification. Experiments are performed on four challenging texture datasets namely, KTH-TIPS-2a, KTH-TIPS-2b, FMD and Texture-10. The experiments clearly demonstrate that our proposed compact multi-texture approach outperforms the single best texture method alone. In all cases, discriminative color names outperforms other color features for texture classification. Finally, we show that combining discriminative color names with compact texture representation outperforms state-of-the-art methods by 7.8%, 4.3% and 5.0% on KTH-TIPS-2a, KTH-TIPS-2b and Texture-10 datasets respectively.

In this paper, we proposed a system of automatically classifying different types of dates from their images. Different dates have various distinguished features that can be useful to recognize a particular date. These features include color, texture, and shape. In the proposed system, a color image of a date is decomposed into its color components. Then, local texture descriptor in the form of local binary pattern (LBP) or Weber local descriptor (WLD) histogram is applied to each of the components to encode the texture pattern of the date. The texture patterns from all the components are fused to describe the image. Fisher discrimination ratio (FDR) based feature selection is utilized to reduce the dimensionality of the feature set. Size and shape features are appended to the texture descriptors to fully describe the date. As a classifier, we use support vector machines. The proposed system achieves more than 98% accuracy to classify the dates.

This study presents a novel texture descriptor for curve matching, called ternary contextualised histogram pattern (TCHP), which is based on the intensity order histogram and local ternary homogeneity patterns. Local ternary homogeneity patterns are firstly adopted to represent the texture features of the curve's neighbourhood, which ensures the robustness and distinctiveness of the descriptor. The proposed TCHP is constructed by three steps: firstly, the curve support region without assigning a dominant orientation is determined and then partitioned into several ordinal bins according to the intensity permutation; then, ternary contextualised histogram (TCH) feature of each point is generated by computing the statistics of the predefined ternary homogeneity patterns; finally, TCHP is achieved by accumulating the TCHs of points in each order bin. Experiments show TCHP can effectively characterise the texture features of the curve's neighbourhood and performs robust to image rotation, viewpoint change and illumination change.

This paper describes an exhaustive comparative analysis and evaluation of different existing texture descriptor algorithms to differentiate between genuine and counterfeit documents. We include in our experiments different categories of algorithms and compare them in different scenarios with several counterfeit datasets, comprising banknotes and identity documents. Computational time in the extraction of each descriptor is important because the final objective is to use it in a real industrial scenario. HoG and CNN based descriptors stands out statistically over the rest in terms of the F1-score/time ratio performance.

The aim of Facial Expression Recognition (FER) is, based on facial information to observe and realize human emotions. It is an exciting and exigent problem to distinguish the human facial expression and emotion. This paper suggests a Gaussian based Edge Detection and Texture Descriptor (GEDTD) for FER. Regarding 8 Gaussian edge descriptors GEDTD is formed. The proposed GEDTD extract both image texture feature and edge direction. Using Local XOR Coding (LXC) scheme the interior and locality pixels of edge response directions are encoded for extraction. Ultimately these features are combined and it forms the feature vector. The expressions of different poses likely disgust, sad, smile and surprise are trained by using Convolution Neural Network (CNN), which differentiates the facial expressions into disgust, sad, smile and surprise. The suggested process increases the recognition accuracy at an important level. The under taken method is an appropriate one for any recognition requirements.

Image tampering/forgery can be accomplished easily using precise image editing software. However, different type of traces is introduced during image forgery process that may helpful in image forgery detection. This paper is an attempt to propose a robust blind image tampering detection technique in accordance with wavelet transform and texture descriptor. Image crucial details are highlighted using shift-invariant stationary wavelet transform. This transform provides more information about image internal statistics in comparison with DWT. Further to convert this information in terms of the feature vector, co-occurrence-based texture operator is used. The linear kernel SVM classifier is utilized to distinguish between pristine and forged images. The effectiveness of the proposed technique is proved on three image forgery evaluation databases, i.e., CASIA v1.0, Columbia and DSO-1.

The selection of the appropriate descriptor for texture analysis in Content Based Image Retrieval (CBIR) has always been a challenging problem in the area of image research. In this paper, we suggest a comparative study of texture analysis methods that allow the selection of suitable combination of descriptors for CBIR. Among the most common descriptors in texture retrieval literature, we find the Co-occurrence matrix, Tamura technique and Log-Gabor filters. These three descriptors are very powerful for CBIR systems. Furthermore, determining the effectiveness of each method at the same time, in the isolation and the combination of several descriptors leads to more accurate results for image retrieval. The proposed techniques are trained and tested for three texture databases. The performance of retrieval is expressed in terms of Precision and Recall and the results showed the superiority of our selected descriptors compared with other proposed method for CBIR in Oracle Database Management System (DBMS).

In this paper, a new texture descriptor based on the local neighborhood intensity difference is proposed for content based image retrieval (CBIR). For computation of texture features like Local Binary Pattern (LBP), the center pixel in a 3*3 window of an image is compared with all the remaining neighbors, one pixel at a time to generate a binary bit pattern. It ignores the effect of the adjacent neighbors of a particular pixel for its binary encoding and also for texture description. The proposed method is based on the concept that neighbors of a particular pixel hold a significant amount of texture information that can be considered for efficient texture representation for CBIR. Taking this into account, we develop a new texture descriptor, named as Local Neighborhood Intensity Pattern (LNIP) which considers the relative intensity difference between a particular pixel and the center pixel by considering its adjacent neighbors and generate a sign and a magnitude pattern. Since sign and magnitude patterns hold complementary information to each other, these two patterns are concatenated into a single feature descriptor to generate a more concrete and useful feature descriptor. The proposed descriptor has been tested for image retrieval on four databases, including three texture image databases - Brodatz texture image database, MIT VisTex database and Salzburg texture database and one face database AT&T face database. The precision and recall values observed on these databases are compared with some state-of-art local patterns. The proposed method showed a significant improvement over many other existing methods., Expert Systems with Applications(Elsevier)

This paper proposes a novel robust texture descriptor based on Gaussian Markov random fields (GMRFs). A spatially localized parameter estimation technique using local linear regression is performed and the distributions of local parameter estimates are constructed to formulate the texture features. The inconsistencies arising in localized parameter estimation are addressed by applying generalized inverse, regularization and an estimation window size selection criterion. The texture descriptors are named as local parameter histograms (LPHs) and are used in texture segmentation with the k-means clustering algorithm. The segmentation results on general texture datasets demonstrate that LPH descriptors significantly improve the performance of classical GMRF features and achieve better results compared to the state-of-the-art texture descriptors based on local feature distributions. Impressive natural image segmentation results are also achieved and comparisons to the other standard natural image segmentation algorithms are also presented. LPH descriptors produce promising texture features that integrate both statistical and structural information about a texture. The region boundary localization can be further improved by integrating colour information and using advanced segmentation algorithms. Display Omitted Distributions of GMRF local parameter estimates as improved texture descriptors.Spatially localized parameter estimation using local linear regression.Approaches to overcome inconsistencies in localized parameter estimation.Proposed descriptors capture both spatial dependencies and distributions of texture.

This paper presents a texture descriptor based on wavelet frame transforms. At each position in the image, and for each resolution level, we consider both vertical and horizontal wavelet detail coefficients as the components of a bivariate random vector. The magnitudes and angles of these vectors are computed. At each level the empirical histogram of magnitudes is modeled by a Generalized Gamma distribution, and the empirical histogram of angles is modeled by a different version of the von Mises distribution that accounts for histograms with 2 modes. Each texture is characterized by few parameters. A new distance is presented (based on the Kullback–Leibler divergence) that allows giving relative importance to each model and to each resolution level. This distance is later conveniently adapted to provide for rotation invariance, by establishing equivalence classes over distributions of angles. Through a broad set of experiments on three different image databases, we demonstrate that our new descriptor and distance measure can be successfully applied in the context of texture retrieval. We compare our system to several relevant methods in this field in terms of retrieval performance and number of parameters used by each method. We also include some classification tests. In all the tests, we obtain superior retrieval rates for a set of fewer parameters involved.

This paper presents new structural statistical matrices which are gray level size zone matrix (SZM) texture descriptor variants. The SZM is based on the cooccurrences of size/intensity of each flat zone (connected pixels with the same gray level). The first improvement increases the information processed by merging multiple gray-level quantizations and reduces the required parameter numbers. New improved descriptors were especially designed for supervised cell texture classification. They are illustrated thanks to two different databases built from quantitative cell biology. The second alternative characterizes the DNA organization during the mitosis, according to zone intensities radial distribution. The third variant is a matrix structure generalization for the fibrous texture analysis, by changing the intensity/size pair into the length/orientation pair of each region.

In this letter, we propose to tackle rotation and scale variance in texture classification at the machine learning level. This is achieved by using image descriptors that interpret these variations as shifts in the feature vector. We model these variations as a covariate shift in the data. This shift is then reduced by minimising the Kullback-Leibler divergence between the true and estimated distributions using importance weights (IW). These IWs are used in support vector machines (SVMs) to formulate the IW-SVMs. The experimental results show that IW-SVMs exhibit good invariance characteristics and outperform other state-of-the-art classification methods. The proposed methodology gives a generic solution that can be applied to any texture descriptor that models the transformations as a shift in the feature vector.

Low-level image feature extraction is the basis of content based image retrieval (CBIR) systems. In that process, the usage of more than one descriptors has tremendous impact on the increasing of system accuracy. Based on that fact, in this paper we combined color and texture feature in the feature extraction process, namely Color Layout Descriptor (CLD) for color feature extraction and Edge Histogram Descriptor (EHD) for texture feature extraction. We measure the system performance on retrieving top-5, top-10, top-15, and top-20 relevant images. We successfully demonstrated in the experiment, that the combination of color and texture descriptor might be improved the performance of retrieval system, significantly. In our proposed system, the combination of CLD and EHD reaches 72.82% in accuracy, using adaptive weight in Late Fusion Method.

This letter presents a rotation invariant descriptor based on the shearlet transform for texture classification. In the presented method, texture images are first decomposed by the shearlet transform, followed by construction of local energy features. Afterwards, the local energy features are quantized and encoded to be rotation invariant. The energy histograms accumulated over all decomposition levels reflect the different energy distributions and form a new image characteristic. Our method can extract more directional features like orientations in images. Moreover, it is robust with respect to noise. Compared to state-of-the-art texture descriptors, the presented method has comparable classification accuracies on the Outex, Brodatz and CUReT texture databases and shows strong robustness on the databases containing additive noise.

An efficient image retrieval scheme to retrieve images is proposed based on the issue of texture and color space features extractions. The algorithm for an effective image retrieval scheme to retrieve images is presented. We propose a scheme using color and texture features and address the unique algorithm to extract the color pixel features by the HSV color space and the texture features of Homogeneous Texture Descriptor (HTD). The proposed scheme transfers each image to a quantized color code using the regulations of the properties in compliance with HSV color space model and then employing the quantized color code along with the texture feature of Homogeneous Texture Descriptor (HTD) to compare the images of database. Experimental of the proposed scheme performed on SIMPLIcity image database to demonstrate more efficient and effective than the conventional schemes.