Local Binary Pattern Texture Research Articles

Facial Makeup Detection Using Multi-Scale Local Binary Patterns and Convolutional Neural Network Fusion

This study presents a novel facial makeup detection scheme using fusion of multi-scale Local Binary Patterns (ms-LBP) texture methods and convolutional neural network (CNN) deep learning extractor. Facial makeups affect the accuracy of face recognition systems due to appearance alteration of individuals. In order to fuse the facial features, the proposed scheme first considers concatenation of extracted global histogram of a whole image using three different LBP operators. The LBP scales are used to extract and then concatenate the local histogram of overlapping and nonoverlapping blocks of images. This way utilizes the advantages of microtexton information of local primitives besides the extracted global textures. Finally, the extracted features using CNN feature extractor are combined with global and local multi-scale textures. In general, CNN deep learning extractor learns high-level discriminative characteristics of images and therefore the proposed system improves the facial makeup detection rate by involving both microtexton and discriminative information of facial images. In addition, the proposed method attempts to select the optimized subset of facial features to increase the detection performance of system by applying Particle Swarm Optimization (PSO) technique after feature level fusion. The paper uses Support Vector Machine (SVM) classifier for classifying the facial vectors into makeup or no-makeup classes. The proposed scheme is then evaluated using YMU, VMU and MIW facial makeup databases with consideration of light, medium and heavy makeups on several datasets. Experimental results analysis clarifies the effectiveness of proposed facial makeup detection framework of this study.

Research on the Identification of Hand-Painted and Machine-Printed Thangka Using CBIR

Thangka is a unique painting art form in Tibetan culture. As Thangka was awarded as the first batch of national intangible cultural heritage, it has been brought into focus. Unfortunately, illegal merchants sell fake Thangkas at high prices for profit. Therefore, identifying hand-painted Thangkas from machine-printed fake Thangkas is important for protecting national intangible cultural heritage. The paper uses Content-Based Image Retrieval (CBIR) techniques to analyze the color, shape, texture, and other characteristics of hand-painted and machine- printed Thangka images, in order to identify Thangkas. Based on the database collected and established by this project team, we use Local Binary Pattern (LBP) texture analysis combined with the color histogram of Hue,aturation, Value (HSV) space, scale invariance, K-Means clustering, perceptual Difference Hash (DHASH) and other algorithms to extract the color lines and texture features of Thangka images, in order to identify hand-painted and machine-printed Thangkas. Three algorithms, LBP algorithm and HSV algorithm and DHASH algorithm, are compared, and the experimental results show that the color histogram algorithm based on HSV space is efficient. This algorithm can be applied broadly to retrieve and identify hand-painted Thangkas and help protect this precious intangible cultural heritage.

Wheat Yellow Rust Disease Infection Type Classification Using Texture Features.

Wheat is a staple crop of Pakistan that covers almost 40% of the cultivated land and contributes almost 3% in the overall Gross Domestic Product (GDP) of Pakistan. However, due to increasing seasonal variation, it was observed that wheat is majorly affected by rust disease, particularly in rain-fed areas. Rust is considered the most harmful fungal disease for wheat, which can cause reductions of 20–30% in wheat yield. Its capability to spread rapidly over time has made its management most challenging, becoming a major threat to food security. In order to counter this threat, precise detection of wheat rust and its infection types is important for minimizing yield losses. For this purpose, we have proposed a framework for classifying wheat yellow rust infection types using machine learning techniques. First, an image dataset of different yellow rust infections was collected using mobile cameras. Six Gray Level Co-occurrence Matrix (GLCM) texture features and four Local Binary Patterns (LBP) texture features were extracted from grayscale images of the collected dataset. In order to classify wheat yellow rust disease into its three classes (healthy, resistant, and susceptible), Decision Tree, Random Forest, Light Gradient Boosting Machine (LightGBM), Extreme Gradient Boosting (XGBoost), and CatBoost were used with (i) GLCM, (ii) LBP, and (iii) combined GLCM-LBP texture features. The results indicate that CatBoost outperformed on GLCM texture features with an accuracy of 92.30%. This accuracy can be further improved by scaling up the dataset and applying deep learning models. The development of the proposed study could be useful for the agricultural community for the early detection of wheat yellow rust infection and assist in taking remedial measures to contain crop yield.

Keypoint detectors and texture analysis based comprehensive comparison in different color spaces for automatic detection of the optic disc in retinal fundus images

Detection of the optic disc which has similar brightness with the hard and soft exudate lesions seen in the early stage of diabetic retinopathy is very difficult due to different light conditions and contrast values. Automatic detection of these lesions by expert systems in the medical field is very important. In this context, we propose a new approach based on the analysis of color spaces, keypoint detectors, and texture for retinal fundus images. If the keypoint information is contained within the actual optic disc region, this is an important consideration for the automated detection of the optic disc. This study can be divided into five sections, respectively, image preprocessing, image processing, keypoint detection, texture analysis, and performance evaluation. The analyses of patch images compatible with the keypoints obtained from the Red–Green–Blue (RGB) image and its color channels were carried out. The performance of the study was validated on the Digital Retinal Images for Vessel Extraction public dataset. According to the results, Local Binary Pattern texture analysis performed in region of interest around keypoints detected by different keypoint detectors presented good performance in RGB and green channel images.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study

To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class. T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier. LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %. LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Comparative Analysis of Different Texture Features in Breast Abnormality Prediction

With advancement in thermal imaging technology, thermogram based methods are becoming increasingly popular for non-invasive and early detection of abnormalities. This paper presents a comparative analysis of breast thermogram texture features to classify malignancy in breast tissues. For this, statistical, Gabor, histogram of gradient and local binary pattern texture features is extracted from pre-processed thermograms individually. Using principal component analysis, these feature sets are reduced in dimension and applied to machine learning, support vector machine algorithm for classification of malignancy. In order to analyse the efficiency of feature sets accuracy, sensitivity, specificity and area under curve performance parameters are computed. The results of best feature set are also compared with other state of the art schemes. This work also amends the literature as it compares different types of texture features used for breast thermograms abnormality detection.

A new modular neural network approach with fuzzy response integration for lung disease classification based on multiple objective feature optimization in chest X-ray images

This paper describes a new hybrid approach, based on modular artificial neural networks with fuzzy logic integration, for the diagnosis of pulmonary diseases such as pneumonia and lung nodules. In particular, the proposed approach analyzes medical images, which are digitized chest X-rays, focusing on a classification method based on descriptors, such as grayscale histogram features, gray-level co-occurrence matrix (GLCM) texture-based features, and local binary pattern texture features. Then, to perform feature reduction, a multi-objective genetic algorithm is used to obtain an optimized neuro-fuzzy classifier, which is able to classify the pathology found in the analyzed chest X-ray. The main contribution of this paper is the proposed modular neural network approach, which divides features to achieve specialized analysis in the modules for digital image analysis and classification. The proposed approach achieves high classification accuracy after evaluating the neuro-fuzzy model with three large datasets of chest X-rays.

The Influence of CLBP Window Size on Urban Vegetation Type Classification Using High Spatial Resolution Satellite Images

Urban vegetation can regulate ecological balance, reduce the influence of urban heat islands, and improve human beings’ mental state. Accordingly, classification of urban vegetation types plays a significant role in urban vegetation research. This paper presents various window sizes of completed local binary pattern (CLBP) texture features classifying urban vegetation based on high spatial-resolution WorldView-2 images in areas of Shanghai (China) and Lianyungang (Jiangsu province, China). To demonstrate the stability and universality of different CLBP window textures, two study areas were selected. Using spectral information alone and spectral information combined with texture information, imagery is classified using random forest (RF) method based on vegetation type, showing that use of spectral information with CLBP window textures can achieve 7.28% greater accuracy than use of only spectral information for urban vegetation type classification, with accuracy greater for single vegetation types than for mixed ones. Optimal window sizes of CLBP textures for grass, shrub, arbor, shrub-grass, arbor-grass, and arbor-shrub-grass are 3 × 3, 3 × 3, 11 × 11, 9 × 9, 9 × 9, 7 × 7 for urban vegetation type classification. Furthermore, optimal CLBP window size is determined by the roughness of vegetation texture.

Weakly supervised detection of central serous chorioretinopathy based on local binary patterns and discrete wavelet transform

Central serous chorioretinopathy (CSCR) is a common fundus disease. Early detection of CSCR is of great importance to prevent visual loss. Therefore, a novel automatic detection method is presented in this paper which integrates technologies including discrete wavelet transform (DWT) image decomposition, local binary patterns (LBP) based texture feature extraction, and multi-instance learning (MIL). LBP is selected due to its robustness to low contrast and low quality images, which can reduce the interference of image itself on the detection method. DWT image decomposition provides high-frequency components with rich details for extracting LBP texture features, which can remove redundant information that is not necessary for diagnosis of CSCR in the raw image. The tedious task of accurately locating and segmenting CSCR lesions is avoided by using MIL. Experiments on 358 optical coherence tomography (OCT) B-scan images demonstrate the effectiveness of our method. Even under the condition of single threshold, the accuracy of 99.58% is obtained at K = 35 by only using a high-frequency feature fusion scheme, which is competitive with the existing methods. Additionally, through further detail innovation, such as multi-threshold optimization (MTO) and integrated decision-making (IDM), the performance of our method is further improved and the detection accuracy is 100% at K = 40.

Klasifikasi Sampah Daur Ulang Menggunakan Support Vector Machine Dengan Fitur Local Binary Pattern

Garbage is one of the problems that always arise in Indonesia and even in the world. Increasingly, the production of waste is increased along with the increase in population and consumption. Therefore, need a prevention to stop wasting or producing garbage through recycle. This research do garbage recycle classification of cardboard, glass, metal, paper and plastic by using Local Binary Pattern (LBP) texture feature extraction methode and Support Vector Machine (SVM) as classification methode. For examination technic and dataset distribution is using K-Fold Cross Validation methode type Leave One Out (LOO). From examination result had been done were using fold 5 until fold 10. Polynomial kernel get highest accuracy result from every fold used with mean point 87.82%. Based on SVM classification examination result whether linear kernel, polynomial nor gaussian by using fold 5 until fold 10. The best accuracy point for cardboard garbage is 96.01%. For glass garbage, the best accuracy point is 90.62%. Then, metal garbage get the best accuracy point 89.72%. While paper garbage with highest accuracy point 96.01%. And plastic garbage with highest accuracy point 87.64%.

Weed Pixel Level Classification Based on Evolving Feature Selection on Local Binary Pattern With Shallow Network Classifier

This paper proposes an evolving feature selection on a rotation-invariant Local Binary Pattern (LBP) with genetic algorithm (GA) and a non-linear classifier to perform pixel-wise classification on biomass pixels. Early true leaf growth of carrots and weeds consists of 60 images provided by a public benchmark Crop/Weed Field Image Dataset (CWFID) [1] was used. The GA encodes LBP feature parameters generated from normalized difference vegetation index (NDVI) image slices as genome consisting of number of neighbours, radius, size of image slice, number of LBP combinations. LBP is a lightweight rotation invariant texture feature descriptor which encodes discriminative local texture information between crops and weeds. The study evaluated multiple ensemble models evolved by GA, where GA evolves the LBP feature parameter selection, and the number of LBP features as input variables used. The classifier can handle crop and weed overlaps, which are often present in commercial fields. Weeds often grow in close proximity and overlap crops and are similar in size, which adds complexity in discriminating them. Our experiments have shown that combinations with a back-propagation neural network with a symmetrical two hidden-layer network achieved the best classification accuracy when compared to other non-linear classifiers. By utilizing a single type of feature (LBP texture feature), our resulting Artificial Neural Network (ANN) ensemble classifier is able to achieve a classification accuracy of 83.5 %.

The use of mobile lidar data and Gaofen-2 image to classify roadside trees

Roadside trees are a vital component of urban greenery and play an important role in intelligent transportation and environmental protection. Quickly and efficiently identifying the spatial distribution of roadside trees is key to providing basic data for urban management and conservation decisions. In this study, we researched the potential of data fusing the Gaofen-2 (GF-2) satellite imagery rich in spectral information and mobile light detection and ranging (lidar) system (MLS) high-precision three-dimensional data to improve roadside tree classification accuracy. Specifically, a normalized digital surface model (nDSM) was derived from the lidar point cloud. GF-2 imagery was fused with an nDSM at the pixel level using the Gram–Schmidt algorithm. Then, samples were set including roadside tree samples from lidar data extracted by random sample consensus and other objects samples from field observation using the Global Positioning System. Finally, we conducted a segmentation process to generate an object-based image and completed the roadside tree classification at object level based on a support vector machine classifier using spectral features and completed local binary pattern (CLBP) texture features. Results show that classification using GF-2 alone and using nDSM alone result in 67.34% and 69.39% overall accuracy respectively with serious misclassification. The fusion image based on GF-2 and nDSM yields 77.55% overall accuracy. This means that the fusion of multi-source data is a great improvement over individual data. After adding the CLBP texture feature to the classification procedure, the classification accuracy of the fusion image is increased to 87.76%. The addition of CLBP texture features can clearly reduce the noise . Our results indicate that the classification of urban roadside trees can be realized by the fusion of satellite data and mobile lidar data with CLBP texture feature using the target-based classification method. Results also suggest that MLS data and CLBP texture features have the potential to effectively and efficiently improve the accuracy of satellite remote sensing classification.

Archaeological surveying with airborne LiDAR and UAV photogrammetry: A comparative analysis at Cahokia Mounds

Remote sensing technologies are becoming indispensable for archaeological fieldwork, helping archaeologists be more efficient and focused in their excavations. Assessing technological feasibility and capability is therefore an essential skill for archaeologists. This research compares digital elevation models from publicly available airplane Light Detection and Ranging (LiDAR) data and unpiloted aerial vehicle (UAV or drone) images to analyze the quality, costs, and benefits of both. Data were collected at the Grand Plaza, which served as the ceremonial center for the civilization at Cahokia Mounds, Illinois, USA, a UNESCO World Heritage Site. GPS elevation data and the Local Binary Pattern (LBP) texture operator were applied to evaluate topographical fidelity in the DEM imagery. We find that photogrammetry is a suitable replacement for LiDAR in specific areas, namely areas with low-lying vegetation. Thus, archaeologists have multiple surveying options and must carefully weigh the technologies available to them based on time and resource constraints.

Multidomain Feature Level Fusion for Classification of Lumbar Intervertebral Disc Using Spine MR Images

Grading of discs is essential for the assessment of degeneration progression which subsequently plays a vital role in decision making in the removal of a disc. In particular, Pfirrmann’s five-scale (1–5) scoring system is widely used in MR image modality for grading the discs. In this study, we have presented a contemporary semiautomatic feature level fusion approach for the classification of inter-vertebral discs. The data of T2-weighted lumbar MR scans in sagittal plane were collected from 120 distinct subjects. In total, 1123 inter-vertebral disc images were obtained upon performing image augmentation. The experts have segregated the discs into five categories as per Pfirrmann’s criteria. This segregation is utilized as ground truth label data for classification. Furthermore, two feature extraction techniques are exploited, one from spatial domain and other follows deep learning process. A popular Local Binary Pattern (LBP) texture descriptor extracts features from spatial domain. In addition, a popular pre-trained Convolution Neural Network (CNN), which acts as a feature extractor, extracts deep features. The training procedure using SVM classifier yields a model built from post-fusion feature vectors. Furthermore, to estimate the model’s performance, a 5-fold cross-validation is performed by computing principal component analysis as well as without dimensionality reduction. Experiment results obtained on our dataset indicate that after dimensionality reduction, SVM classifier with various kernel functions yields the accuracy up to 92%. A quantitative analysis of the classifier model is presented for parameters, namely – Accuracy, Area Under Curve (AUC), Specificity, Sensitivity, and F1 measure.

Brain Tumor Classification Using Enhanced Statistical Texture Features

ABSTRACT Radiology is a vast subject and we require more knowledge and understanding for exact detection of tumor in medical science. Thus a need for tumor detection system overcomes the shortage of skilled radiologists. Biomedical image processing using Magnetic Resonance Imaging (MRI) makes the task of detection and localization of brain tumor. In this article, a brain tumor segmentation and detection approach has been designed using MRI sequence images as input image for defining the tumor region. This process is difficult due to the large diversity in the presences of tumor tissues with respect to different patients and in most of the cases similarity within the normal tissues makes the task difficult. The main goal is to classify the brain into the presence of brain tumor or a healthy brain. The proposed system provides Edge-based Contourlet Transformation for multiple input image registration, fusion and pre-processing, for Region of Interest(ROI) of tumor region the region-growing segmentation algorithm provides accurate boundaries, in feature extraction the Gray Level Run Length Matrix (GLRLM) and Center-Symmetric Local Binary Patterns(CSLBP) texture features are combined for efficient brain tumor detection and for classification Adopting Neural Network(ANN) techniques is carried out. The experimental results of our proposed method are compared with different algorithms in terms of accuracy.

A New Statistical Framework for Corpus Callosum Sub-Region Characterization Based on LBP Texture in Patients With Parkinsonian Disorders: A Pilot Study

PurposeThe study is conducted to identify the best corpus callosum (CC) sub-region that corresponds to highest callosal tissue alteration occurred due to Parkinsonism. In this regard the efficacy of local binary pattern (LBP) based texture analysis (TA) of CC is performed to quantify the changes in topographical distribution of callosal fiber connected to different regions of cortex. The extent of highest texture alteration in CC is used for differential diagnosis.Materials and MethodsStudy included subjects with Parkinson’s disease (PD) (n = 20), and atypical Parkinsonian disorders – multiple system atrophy (MSA) (n = 20), Progressive supranuclear palsy (PSP) (n = 20), and healthy controls (n = 20). For each subject, we have automated the ROI extraction within mid-sagittal CC, followed by LBP TA. Two-class support vector machine (SVM) classification for each disorder as against HC is performed using extracted LBP features like energy and entropy. Correct classification ratio (CCR) is computed as the fraction of correctly classified ROIs at each of the CC sub-regions based on well-known Witelson and Hofer schemes. Based on CCR values, the “Scatter Index (SI)” is proposed to capture how localized (closer to 0) or scattered (closer to 1) the textural changes are among the CC sub-regions, across all subjects per class. The CCR values are further utilized to classify the disease groups.ResultsHighest alteration of texture is observed in mid-body of CC. The consistency of this finding is quantified using SI for all subjects in a specific class that results more localized textural changes in PSP (15%) and MSA (25%), in comparison to PD (47%). Classification among disease groups results maximum classification accuracy of 90% in classifying PSP from PD-NC.ConclusionOur result demonstrates the efficacy of proposed methodology in analyzing tissue alteration in MRI of Parkinsonian disorders and thus has potential to become valuable tool in computer aided differential diagnosis.

Automatic Detection for Battery Leakage Based on DenseNet

This article proposes a detection method based on thermal imaging for lead acid-battery leakage. First of all, thermal images were obtained by scanning the lead acid-battery with an infrared camera, and the images were categorized into the two sets of train and test. Then, two methods were introduced to analyze the thermal images to determine whether there was a leakage in the battery. One method used Support Vector Machine (SVM) to train the Local Binary Pattern (LBP) texture features of the images. The other method used deep learning to detect images, and trained the obtained data by DenseNet. The results demonstrate that the two methods are accurate, and show feasibility of thermal imaging to detect lead acid-battery leakage.

Improving Seismic Data Resolution With Deep Generative Networks

Noisy traces, gaps in coverage, or irregular/inadequate trace spacing are common problems in both land and marine surveys, possibly hindering the geological interpretation of an area of interest. This problem has been typically addressed in the literature using prestack data; however, prestack data are not always available. As an alternative, poststack interpolations may aid the geological interpretation by increasing the spatial density of a seismic section and can also be used to reconstruct entire sections by interpolating neighboring traces, reducing field costs. In this letter, we evaluate the performance of conditional Generative Adversarial Networks (cGANs) as an interpolation tool for improving seismic data resolution on a public poststack seismic data set and compare our results with the traditional cubic interpolation. To perform the comparisons, we used structural similarity (SSIM), mean squared error (mse), and local binary patterns (LBPs) texture descriptor. The results show that cGANs outperform traditional algorithms by up to 72% and that the texture descriptor was able to better capture image similarities, producing results more coherent with the visual perception.

A novel method for digital image copy-move forgery detection and localization using evolving cellular automata and local binary patterns

Copy-Move Forgery Detection (CMFD) methods aim to forensically analyze a digital image for a possible content duplication manipulation. In the past, many block-based algorithms have been proposed for detection and localization of CMF. However, the existing solutions show limited efficacy for images compressed in JPEG and lack robustness against post-processing attacks such as noise addition, blurring, etc. To address this problem, we propose a new block-based passive method for detection and localization of CMF in this paper. Passive methods, as opposed to active methods, are used to authenticate the image content in the absence of any pre-embedded information such as watermarks. In our proposed scheme, a suspicious input image to be analyzed is first low pass filtered and converted to Local Binary Patterns (LBP) image. The LBP texture image is then divided into overlapping blocks. Next, a compact five-dimensional feature vector is extracted from each block by employing thresholding and Cellular Automata. The set of feature vectors is sorted lexicographically to bring the copy-pasted blocks nearer to each other. Finally, the feature matching step is used to reveal the duplicate blocks. Our experimental results indicate that the proposed method performs exceptionally well relative to other state-of-art-methods, under different image manipulation scenarios.

Performant Retrieval Image using Rectangular Mask and Combination of Color, Texture and Shape Descriptors

The evolution of computer technologies has led to the growth of digital images, which has made the search for similar images in this volume of data a very important research component. Since several works have proposed image search systems entitled CBIR (Content-Based Image Retrieval). This paper presents a new and powerful method for creating CBIR in order to improve the accuracy of search through visual content. The originality of our method lies in its invariance to the rotation of images queries. She consists of applying rectangular masks of different size on the image, and extracting the color descriptor from the visible region on the mask, and then combining the result descriptor to the Uniform Local Binary Pattern (ULBP) texture features and add canny edge features. We compare the query features to the extract ones, using metric distance. We evaluate our techniques using Corel1K and Ukbench dataset. The average precision measured gives good results comparing to the others existing retrieval systems.