Brodatz Texture Research Articles

Graph- and Machine-Learning-Based Texture Classification

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification.

Is it blur or is it texture? An analysis of computational blur and texture measures commonly used in medical image analysis.

In this work, we endeavor to investigate how texture information may contribute to the response of a blur measure (BM) with motivation rooted in mammography. This is vital as the interpretation of the BM is typically not evaluated with respect to texture present in an image. We are particularly concerned with lower scales of blur () as this blur is least likely to be detected but can still have a detrimental effect on detectability of microcalcifications. Three sets of linear models, where BM response was modeled as a linear combination of texture information determined by texture measures (TMs), were constructed from three different datasets of equal-blur-level images; one of computer-generated mammogram-like clustered lumpy background (CLB) images and two image sets derived from the Brodatz texture images. The linear models were refined by removing those TMs that are not significantly non-zero across all three datasets for each BM. We use five levels of Gaussian blur to blur the CLB images and assess the ability of the BMs and TMs to separate the images based on blur level. We found that many TMs used frequently in the reduced linear models, mimicked the structure of the BMs that they modeled. Surprisingly, while none of the BMs could separate the CLB images across all levels of blur, a group of TMs could. These TMs occurred infrequently in the reduced linear models meaning that they rely on different information compared with that used by the BMs. These results confirm our hypothesis that BMs can be influenced by texture information in an image. That a subset of TMs performed better than all BMs on the blur classification problem with the CLB images further shows that conventional BMs may not be the optimal tool for blur classification in mammogram images.

Multi-Resolution Feature Embedded Level Set Model for Crosshatched Texture Segmentation

In image processing applications, texture is the most important element utilized by human visual systems for distinguishing dissimilar objects in a scene. In this research article, a variational model based on the level set is implemented for crosshatched texture segmentation. In this study, the proposed model’s performance is validated on the Brodatz texture dataset. The cross-hatched texture segmentation in the lower resolution texture images is difficult, due to the computational and memory requirements. The aforementioned issue has been resolved by implementing a variational model based on the level set that enables efficient segmentation in both low and high-resolution images with automatic selection of the filter size. In the proposed model, the multi-resolution feature obtained from the frequency domain filters enhances the dissimilarity between the regions of crosshatched textures that have low-intensity variations. Then, the resultant images are integrated with a level set-based active contour model that addresses the segmentation of crosshatched texture images. The noise added during the segmentation process is eliminated by morphological processing. The experiments conducted on the Brodatz texture dataset demonstrated the effectiveness of the proposed model, and the obtained results are validated in terms of Intersection over the Union (IoU) index, accuracy, precision, f1-score and recall. The extensive experimental investigation shows that the proposed model effectively segments the region of interest in close correspondence with the original image. The proposed segmentation model with a multi-support vector machine has achieved a classification accuracy of 99.82%, which is superior to the comparative model (modified convolutional neural network with whale optimization algorithm). The proposed model almost showed a 0.11% improvement in classification accuracy related to the existing model.

Enhanced prediction using deep neural network-based image classification

ABSTRACT The need for deep convolutional neural network is increasing for medical image classification because it provides good performance. This work elucidates the significance of convolutional neural network in making effective detection of clinical diseases by categorizing the clinical images in an organized manner. Clinical diseases are difficult to predict and interpret. To predict diseases from medical images, the Stochastic Multinomial Logarithmic (SML) based image classification method is proposed. To effectively eliminate noise from images, edge-boosting locally adapted space-variant filters are first applied to the texture and medical MRI, and CT images. The SML approach is used to improve feature classification and disease prediction. Accuracy, Peak Signal-to-Noise Ratio (PSNR), precision, recall and specificity performances of the proposed approach are compared with surviving methods. The proposed method produces enhanced performance compared to the existing ones with improved accuracies of 95.8% and 96.2 % respectively, for Brodatz texture and brain MRI, CT images.

Restored texture segmentation using Markov random fields.

Texture segmentation plays a crucial role in the domain of image analysis and its recognition. Noise is inextricably linked to images, just like it is with every signal received by sensing, which has an impact on how well the segmentation process performs in general. Recent literature reveals that the research community has started recognizing the domain of noisy texture segmentation for its work towards solutions for the automated quality inspection of objects, decision support for biomedical images, facial expressions identification, retrieving image data from a huge dataset and many others. Motivated by the latest work on noisy textures, during our work being presented here, Brodatz and Prague texture images are contaminated with Gaussian and salt-n-pepper noise. A three-phase approach is developed for the segmentation of textures contaminated by noise. In the first phase, these contaminated images are restored using techniques with excellent performance as per the recent literature. In the remaining two phases, segmentation of the restored textures is carried out by a novel technique developed using Markov Random Fields (MRF) and objective customization of the Median Filter based on segmentation performance metrics. When the proposed approach is evaluated on Brodatz textures, an improvement of up to 16% segmentation accuracy for salt-n-pepper noise with 70% noise density and 15.1% accuracy for Gaussian noise (with a variance of 50) has been made in comparison with the benchmark approaches. On Prague textures, accuracy is improved by 4.08% for Gaussian noise (with variance 10) and by 2.47% for salt-n-pepper noise with 20% noise density. The approach in the present study can be applied to a diversified class of image analysis applications spanning a wide spectrum such as satellite images, medical images, industrial inspection, geo-informatics, etc.

Two-Dimensional EspEn: A New Approach to Analyze Image Texture by Irregularity.

Image processing has played a relevant role in various industries, where the main challenge is to extract specific features from images. Specifically, texture characterizes the phenomenon of the occurrence of a pattern along the spatial distribution, taking into account the intensities of the pixels for which it has been applied in classification and segmentation tasks. Therefore, several feature extraction methods have been proposed in recent decades, but few of them rely on entropy, which is a measure of uncertainty. Moreover, entropy algorithms have been little explored in bidimensional data. Nevertheless, there is a growing interest in developing algorithms to solve current limits, since Shannon Entropy does not consider spatial information, and SampEn2D generates unreliable values in small sizes. We introduce a proposed algorithm, EspEn (Espinosa Entropy), to measure the irregularity present in two-dimensional data, where the calculation requires setting the parameters as follows: m (length of square window), r (tolerance threshold), and (percentage of similarity). Three experiments were performed; the first two were on simulated images contaminated with different noise levels. The last experiment was with grayscale images from the Normalized Brodatz Texture database (NBT). First, we compared the performance of EspEn against the entropy of Shannon and SampEn2D. Second, we evaluated the dependence of EspEn on variations of the values of the parameters m, r, and . Third, we evaluated the EspEn algorithm on NBT images. The results revealed that EspEn could discriminate images with different size and degrees of noise. Finally, EspEn provides an alternative algorithm to quantify the irregularity in 2D data; the recommended parameters for better performance are m = 3, r = 20, and = 0.7.

Directional Magnitude Local Hexadecimal Patterns: A Novel Texture Feature Descriptor for Content-Based Image Retrieval

Social media platforms such as Twitter, Facebook, and Flicker, and the evolution of digital image capturing devices have resulted in the generation of a massive number of images. Thus, we experienced an exponential growth in digital image repositories in the last decade. Content-based image retrieval (CBIR) has been extensively employed to reduce the dependency on textual annotations for image searching. Effective feature descriptor is mandatory to retrieve the most relevant images from the repository. Additionally, CBIR methods often experience the semantic gap problem, which must also be addressed. In this paper, we propose a novel texture descriptor, Directional Magnitude Local Hexadecimal Patterns (DMLHP), based on the texture orientation and magnitude to retrieve the most relevant images. The objective of the proposed feature descriptor is to examine the relationship between the neighboring pixels and their adjacent neighbors based on texture orientation and magnitude. Our DMLHP texture descriptor is capable of capturing the texture and semantic information of the images effectively with the same visual content. Furthermore, the proposed method employs a learning-based approach to lessen the semantic gap problem and to improve the understanding of the contents of query images to retrieve the most relevant images. The presented descriptor provides remarkable results by achieving the average retrieval precision (ARP) of 66%, 92%, 83%, average retrieval recall (ARR) of 66%, 92%, 83%, average retrieval specificity (ARS) of 99%, 99%, 76%, and average retrieval accuracy (ARA) of 98%, 99%, 85% on the AT&T, MIT Vistex, and Brodatz Texture image repositories, respectively. Our experiments reveal that the proposed DMLHP descriptor achieves far better performance, i.e., 95% on AT&T, 92% on BT, and 99% on MIT Vistex, when used with a learning-based approach over a non-learning-based approach (similarity measure). Experimental results show that the proposed texture descriptor outperforms state-of-the-art descriptors such as LNIP, LTriDP, LNDP, LDGP, LEPSEG, and CSLBP for CBIR.

Texture Classification using Angular and Radial Bins in Transformed Domain

Texture is generally recognized as fundamental to perceptions. There is no precise definition or characterization available in practice. Texture recognition has many applications in areas such as medical image analysis, remote sensing, and robotic vision. Various approaches such as statistical, structural, and spectral have been suggested in the literature. In this paper we propose a method for texture feature extraction. We transform the image into a two-dimensional Discrete Cosine Transform (DCT) and extract features using the ring and wedge bins in the DCT plane. These features are based on texture properties such as coarseness, smoothness, graininess, and directivity of the texture pattern in the image. We develop a model to classify texture images using extracted features. We use three classifiers: the Decision Tree, Support Vector Machine (SVM), and Logarithmic Regression (LR). To test our approach, we use Brodatz texture image data set consisting of 111 images of different texture patterns. Classification results such as accuracy and F-score obtained from the three classifiers are presented in the paper.

Texture classification based on image (natural and horizontal) visibility graph constructing methods.

Texture classification is widely used in image analysis and some other related fields. In this paper, we designed a texture classification algorithm, named by TCIVG (Texture Classification based on Image Visibility Graph), based on a newly proposed image visibility graph network constructing method by Lacasa et al. By using TCIVG on a Brodatz texture image database, the whole procedure is illustrated. First, each texture image in the image database was transformed to an associated image natural visibility graph network and an image horizontal visibility graph network. Then, the degree distribution measure [P(k)] was extracted as a key characteristic parameter to different classifiers. Numerical experiments show that for artificial texture images, a 100% classification accuracy can be obtained by means of a quadratic discriminant based on natural TCIVG. For natural texture images, 94.80% classification accuracy can be obtained by a linear SVM (Support Vector Machine) based on horizontal TCIVG. Our results are better than that reported in some existing literature studies based on the same image database.

A Class-Independent Texture-Separation Method Based on a Pixel-Wise Binary Classification.

Texture segmentation is a challenging problem in computer vision due to the subjective nature of textures, the variability in which they occur in images, their dependence on scale and illumination variation, and the lack of a precise definition in the literature. This paper proposes a method to segment textures through a binary pixel-wise classification, thereby without the need for a predefined number of textures classes. Using a convolutional neural network, with an encoder–decoder architecture, each pixel is classified as being inside an internal texture region or in a border between two different textures. The network is trained using the Prague Texture Segmentation Datagenerator and Benchmark and tested using the same dataset, besides the Brodatz textures dataset, and the Describable Texture Dataset. The method is also evaluated on the separation of regions in images from different applications, namely remote sensing images and H&E-stained tissue images. It is shown that the method has a good performance on different test sets, can precisely identify borders between texture regions and does not suffer from over-segmentation.

Comparative analysis of texture feature extraction techniques for rice grain classification

Classifications of eight different varieties of rice grain are discussed in this study based on various texture models. Four local texture feature extraction techniques are proposed and three sets of texture features (SET-A, SET-B and SET-C) are formed, for the classification task. Performances of the proposed feature sets are compared with the existing techniques based on, run length matrix, co-occurrence matrix, size zone matrix, neighbourhood grey tone difference matrix and wavelet decomposition, towards classification of rice grain using a back propagation neural network (BPNN). The proposed techniques are also tested against publicly available data from Brodatz's texture data set and their results are compared with other techniques. The classification accuracy by the BPNN classifier is also compared with other statistical classifiers namely, K-nearest neighbour, linear discriminant classifier and Naive Bayes classifier. It is found that, the proposed feature sets yield better classification results on both rice data and Brodatz's data. Results show that, feature SET-B, is able to classify rice grain with an average classification accuracy of 99.63% with a minimum of six features.

Maximal multi-channel local binary pattern with colour information for CBIR

Content Based Image Retrieval (CBIR) focuses on retrieving images from repositories based on visual features extracted from the images. Texture and colour are one of the popularly used feature combination in CBIR. A major challenge in colour image retrieval is the characterization of features of the constituent channels and their integration. The commonly adopted methodology include extraction of features of various channels followed by their concatenation. However, the resulting image feature vector is generally of high dimensionality. To address this problem, in this paper a texture-colour descriptor is proposed integrating the multi-channel features. For texture computation, a fixed sized local intensity based descriptor, Maximal Multi-channel Local Binary Pattern (MMLBP), which integrates the multi-channel local binary information through an adder-map followed by thresholding is introduced. The histogram of the obtained patterns is used for representing the image texture. Colour information is captured by quantizing the RGB colour space and is represented with histogram. The colour-texture descriptors are further fused to characterize the images. The efficacy of the descriptor is evaluated by carrying out retrieval on benchmarked datasets for image retrieval such as Wang’s 1 K, Corel 5 K, Corel 10 K, Coloured Brodatz Texture and Zubud, using precision and recall measures as evaluation metrics. It is observed that the proposed descriptor presents improved retrieval performance over the databases under consideration and outperforms other descriptors.

Does image normalization and intensity resolution impact texture classification?

Image texture is a very important component in many types of images, including medical images. Medical images are often corrupted by noise and affected by artifacts. Some of the texture-based features that should describe the structure of the tissue under examination may also reflect, for example, the uneven sensitivity of the scanner within the tissue region. This in turn may lead to an inappropriate description of the tissue or incorrect classification. To limit these phenomena, the analyzed regions of interest are normalized. In texture analysis methods, image intensity normalization is usually followed by a reduction in the number of levels coding the intensity. The aim of this work was to analyze the impact of different image normalization methods and the number of intensity levels on texture classification, taking into account noise and artifacts related to uneven background brightness distribution. Analyses were performed on four sets of images: modified Brodatz textures, kidney images obtained by means of dynamic contrast-enhanced magnetic resonance imaging, shoulder images acquired as T2-weighted magnetic resonance images and CT heart and thorax images. The results will be of use for choosing a particular method of image normalization, based on the types of noise and distortion present in the images.

Combining Statistical Features and Local Pattern Features for Texture Image Retrieval

The complementary fusion of global and local features can effectively improve the performance of image retrieval. This article proposes a new local texture descriptor, combined with statistical modeling in transform domain for texture image retrieval. The proposed local descriptor calculates the eight directions of the central pixel by using the relationship between the central pixel and the neighboring pixels in six directions, which is called the local eight direction pattern (LEDP). In the texture image retrieval system of this article, the feature extraction part combines global statistical features and local pattern features. Among them, both the relative magnitude (RM) sub-band coefficients and relative phase (RP) sub-band coefficients are modeled as wrapped Cauchy (WC) distribution in the dual-tree complex wavelet transform (DTCWT) domain, and the global statistical features employ the parameters of this model; while the local pattern features respectively choose the local binary pattern (LBP) histogram features in the spatial domain and the LEDP histogram features of each direction sub-band in the DTCWT domain. On the other hand, the similarity measurement selects matching distances for different features and combines them in the form of convex linear optimization. Texture image retrieval experiments are conducted in the Corel-1k database (DB1), Brodatz texture database (DB2) and MIT VisTex texture database (DB3), respectively. Experimental results show that, compared with the best existing methods, the approach proposed in this article has achieved better retrieval performance.

A novel automatic image segmentation method for Chinese literati paintings using multi-view fuzzy clustering technology

Under the background of contemporary cultural protection and dynamic inheritance, the interpretation and re-expression of the artistic connotations of Chinese literati paintings have become the main direction of heritage research. Digital technology and multimedia expression have become important means of cultural expression and transmission. Most Chinese literati paintings are ink paintings, and the particularity of ink painting makes it difficult to decompose and extract the screen content by simple means, which has caused difficulties in digitization, re-expression, and public interpretation to some extent. To solve this problem, a new robust multi-view (M-V) fuzzy clustering algorithm is proposed for image segmentation of Chinese literati paintings to achieve effective decomposition and extraction of ancient paintings. Through the effective decomposition and extraction of literati paintings, the electronic and digital transformation and preservation of literati paintings can be realized. This kind of preservation method, more than traditional scanning, can preserve the artistry of literati paintings, which is of great value for the re-expression and dissemination of cultural heritage. Experiments on noise-added Brodatz texture images show that the proposed algorithm is insensitive to noise and has good robustness. Experiments on real Chinese literati paintings show that the proposed algorithm can effectively segment literati paintings and further realize their decomposition and extraction.

Texture classification using the rotational-invariant local symmetric tetra pattern

Feature extraction plays a key role in pattern recognition problems. The texture feature is an important feature which helps to describe an image with textural information. A new texture descriptor, the Local Symmetric Tetra Pattern (LSTP), is proposed in this work. This descriptor is developed for the local description of an image. It considers not only the surrounding eight neighbors, but also the eight pixels at the next level to describe the texture efficiently. For every pixel, the maximum edge value, the number of negative sign bits and the number of positive sign bits for each degree of symmetry are computed. Image classification is experimented using the Original Brodatz, Outex and Kylberg Texture Dataset v.1.0 databases. The investigation results are compared with existing method which shows promising achievement of the proposed techniques in terms of their evaluation measures. It is also found that the proposed texture descriptor is rotationally invariant.

Content-based image retrieval using local texture features in distributed environment

In this paper, we propose novel content-based image retrieval (CBIR) algorithms using Local Octa Patterns (LOtP), Local Hexadeca Patterns (LHdP) and Direction Encoded Local Binary Pattern (DELBP). LOtP and LHdP encode the relationship between center pixel and its neighbors based on the pixels’ direction obtained by considering the horizontal, vertical and diagonal pixels for derivative calculations. In DELBP, direction of a referenced pixel is determined by considering every neighboring pixel for derivative calculations which results in 256 directions. For this resultant direction encoded image, we have obtained LBP which is considered as feature vector. The proposed method’s performance is compared to that of Local Tetra Patterns (LTrP) using benchmark image databases viz., Corel 1000 (DB1) and Brodatz textures (DB2). Performance analysis shows that LOtP improves the average precision from 59.31% to 64.36% on DB1, and from 83.24% to 85.95% on DB2, LHdP improves it to 65.82% on DB1 and to 87.49% on DB2 and DELBP improves it to 60.35% on DB1 and to 86.12% on DB2 as compared to that of LTrP. Also, DELBP reduces the feature vector length by 66.62% as compared to that of LTrP. To reduce the retrieval time, the proposed algorithms are implemented on a Hadoop cluster consisting of 116 nodes and tested using Corel 10K (DB3), Mirflickr 100,000 (DB4) and ImageNet 511,380 (DB5) databases.

Taking advantage of motif matrix inference for rotated image indexing and retrieval

With the rapid development of information technology, the sizes of digital libraries become larger and larger. How to quickly and effectively search the desired images in huge digital libraries becomes the challenge needed to resolve with high priority. In this study, we firstly propose two motif-based matrices, i.e., the motif average matrix (MAM) and motif excessive matrix (MEM), to describe the color and texture features of an image. Subsequently, in terms of the inference of MAM and MEM, a motif matrix (MM) is further proposed to index rotated images and resolve the issue of rotated image retrieval. That is, in the light of such an inference, MM incorporates the color and texture characters and reveals the consistent relevance between the original and rotated images, which can be effectively used for rotated image retrieval. To extensively test the performance of our method, we carry out the experiments on the benchmark Corel image dataset, Brodatz texture image dataset, WIPO global brand dataset, and the experimental results show that our approach of motif matrix inference improves the retrieval performance in comparison with the state-of-the-art image retrieval approaches.

PLT‐based spectral features for texture image retrieval

Effective texture feature is an essential component in any content-based image retrieval system. In this study, new texture features based on image enhancement technique are presented. The authors have effectively exploited power-law transform (PLT) to extract new spectral texture features called PLT-based spectral features. Extensive experiments on the Brodatz texture database and Salzburg Textures image database prove the effectiveness of the proposed techniques and show that the proposed features significantly outperform the widely used Gabor and curvelet features. The proposed features are also compared with recently published Gaussian copula models of Gabor feature and local tetra patterns (LTrP). The experimental results confirm that the proposed features have more tolerance to scale, orientation and illumination distortion than the state-of-the-art Gabor, curvelet, Gaussian copula models of Gabor and LTrPs.

Local Neighborhood Intensity Pattern–A new texture feature descriptor for image retrieval

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 significant amount of texture information that can be considered for efficient texture representation for CBIR. The main impact of utilizing the mutual relationship among adjacent neighbors is that we do not rely on the sign of the intensity difference between central pixel and one of its neighbors (Ii) only, rather we take into account the sign of difference values between Ii and its adjacent neighbors along with the central pixels and same set of neighbors of Ii. This makes our pattern more resistant to illumination changes. Moreover, most of the local patterns including LBP concentrates mainly on the sign information and thus ignores the magnitude. The magnitude information which plays an auxiliary role to supply complementary information of texture descriptor, is integrated in our approach by considering the mean of absolute deviation about each pixel Ii from its adjacent neighbors. 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. Finally, the sign pattern (LNIPS) and the magnitude pattern (LNIPM) are concatenated into a single feature descriptor to generate a more effective 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.