Combination Of Local Binary Pattern Research Articles

Deep and hybrid learning of MRI diagnosis for early detection of the progression stages in Alzheimer’s disease

Alzheimer’s, or so-called dementia, is one of the types of diseases that affects brain cells and causes memory loss, difficulty in thinking, and forgetfulness. Thus far, there is no effective treatment for AD, but treatment could be helpful in impeding the progression of the disease. Therefore, early AD diagnosis is effective in limiting the disease from progressing to advanced and dangerous stages. Physicians and radiologists face difficulties in diagnosing healthy nerve cells from soft tissue, and it requires substantial expertise and a long time to decipher the MRI images. Thus, artificial intelligence techniques can play a key role in diagnosing MRI images for early detection of AD. In this study, four proposed systems with different methodologies and materials for tracking the stages of AD development are presented. The first proposed system is to classify a data set using artificial neural networks (ANNs) and feed-forward neural networks (FFNN) based on the features extracted in a hybrid manner by using a combination of Local Binary Pattern (LBP), Discrete Wavelet Transform (DWT), and Gray Level Co-occurrence Matrix (GLCM) algorithms. The second proposed system is to classify the data set using two deep learning models – ResNet-18 and AlexNet – that are pre-trained based on deep feature map extraction. The third proposed system is to diagnose the data set using a hybrid technology between ResNet-18 and AlexNet models to extract feature maps and machine learning (SVM) to classify feature maps. The fourth proposed system diagnoses the data set using ANN and FFNN algorithms based on the hybrid features of ResNet-18 and AlexNet deep learning models and traditional algorithms (LBP, DWT, and GLCM). All the proposed techniques achieved superior results in the diagnosis of MRI images for early detection of AD. The FFNN algorithms based on the hybrid features extracted by ResNet-18 with features extracted using traditional algorithms achieved an accuracy of 99.8%, precision of 99.9%, sensitivity of 99.75%, specificity of 100%, and AUC of 99.94%.

Algorithms and programs based on neural networks and local binary patterns approaches for monitoring plankton populations in sea systems

The work is devoted to the method of multispectral space images analyzing of aquatic coastal systems for identifying phytoplankton populations of complicated structures: determining their boundaries, distributing color gradations and, based on this, determining the distribution of phytoplankton concentrations within patches and the location of the "mass" center. A combination of local binary patterns (LBP) and neuralnetworks methods is considered. Due to these characteristics it is possible, basing on a series of processed images of the same water area for different time points (dates), to determine the changing rate in the spots boundaries and their concentrations, the shift of the mass center which are influenced by the aquatic environment movement and the processes of phytoplankton growth and death. The results of the work allow us to determine the Azov Sea state. Experimental data of the program are given in confirmation part.

Hybrid Feature Extractions and CNN for Enhanced Periocular Identification During Covid-19

The global pandemic of novel coronavirus that started in 2019 has seriously affected daily lives and placed everyone in a panic condition. Widespread coronavirus led to the adoption of social distancing and people avoiding unnecessary physical contact with each other. The present situation advocates the requirement of a contactless biometric system that could be used in future authentication systems which makes fingerprint-based person identification ineffective. Periocular biometric is the solution because it does not require physical contact and is able to identify people wearing face masks. However, the periocular biometric region is a small area, and extraction of the required feature is the point of concern. This paper has proposed adopted multiple features and emphasis on the periocular region. In the proposed approach, combination of local binary pattern (LBP), color histogram and features in frequency domain have been used with deep learning algorithms for classification. Hence, we extract three types of features for the classification of periocular regions for biometric. The LBP represents the textual features of the iris while the color histogram represents the frequencies of pixel values in the RGB channel. In order to extract the frequency domain features, the wavelet transformation is obtained. By learning from these features, a convolutional neural network (CNN) becomes able to discriminate the features and can provide better recognition results. The proposed approach achieved the highest accuracy rates with the lowest false person identification.

Automated detection of former field systems from airborne laser scanning data: a new approach for Historical Ecology

Former field systems (FFS) are the most widespread traces of ancient activities in present European landscapes and represent significant perturbations to ecosystems. Through its ability to penetrate forest canopies and detect microlandforms, Airborne Laser Scanning data reveal archaeological relics over large areas, from periods older than the first available Historical Topographic Maps. Mapping these traces from ALS-derived data (e.g. Digital Elevation Model (DEM)) thus allows for a determination of a new temporal baseline in order to evaluate the effects of a longer history on current patterns of biodiversity. Here, we evaluate the ability of traditional machine learning (Random Forest-RF) and deep learning (Fully Connected Networks-FCN) models to detect Medieval Terraced slopes and Ridges and Furrows (RaF) from an ALS-derived DEM in the southern Vosges (1462 km2). We used a combination of Local Binary Patterns and topographical metrics to measure properties of FFS and to train detection models. We then assessed the relative performance of each model semantically and spatially. Our results demonstrated the high suitability of our approach for reproducing major trends in the landscape with a high level of similarity between the predicted and reference spatial patterns (Structural Similarity Index - SSIM > 0.75). RF outperformed FCN for Terraced Slopes, whilst minimizing the false positive rate. FCN slightly outperformed RF for the RaF dataset but showed promising abilities to survey unseen data with a low sensitivity to annotation errors. We suggest that this approach has the potential to offer new spatio-temporal possibilities in Historical Ecology studies as a means of automatically detecting past archaeological ecosystems from a landscape to a regional scale.

An effective approach to unmanned aerial vehicle navigation using visual topological map in outdoor and indoor environments

Unmanned Aerial Vehicles are constantly being using in professional activities that require higher precision in navigating and positioning the aircraft during operation. Advanced location technologies such as Global Navigation Satellite System and Real-Time Kinematic are widely used, however, they depend on an area with transmission coverage. In this approach, this article presents a visual navigation methodology based on topological maps. We compared the performance of consolidated classifiers such as Bayesian classifier, k-nearest neighbor, Multilayer Perceptron, Optimal Path Forest and Support Vector Machines (SVM). They are evaluated with attributes returned by last generation resource extractors such as Fourier, Gray Level Co-Occurrence and Local Binary Patterns (LBP). After analyzing the results we found that the combination of LBP and SVM obtained the best values in the evaluation metrics considered, among them, 99.99% Specificity and 99.98% Precision in the navigation process. SVM reached 5.49787 s in combination with LBP completes the training in 5.49787 s. Concerning the testing time, SVM achieving 80.91 ms in association with LBP.

An Intelligent Multi-Resolutional and Rotational Invariant Texture Descriptor for Image Retrieval Systems

To find out the identical or comparable images from the large rotated databases with higher retrieval accuracy and lesser time is the challenging task in Content based Image Retrieval systems (CBIR). Considering this problem, an intelligent and efficient technique is proposed for texture based images. In this method, firstly a new joint feature vector is created which inherits the properties of Local binary pattern (LBP) which has steadiness regarding changes in illumination and rotation and discrete wavelet transform (DWT) which is multi-resolutional and multi-oriented along with higher directionality. Secondly, after the creation of hybrid feature vector, to increase the accuracy of the system, classifiers are employed on the combination of LBP and DWT. The performance of two machine learning classifiers is proposed here which are Support Vector Machine (SVM) and Extreme learning machine (ELM). Both proposed methods P1 (LBP+DWT+SVM) and P2 (LBP+DWT+ELM) are tested on rotated Brodatz dataset consisting of 1456 texture images and MIT VisTex dataset of 640 images. In both experiments the results of both the proposed methods are much better than simple combination of DWT +LBP and much other state of art methods in terms of precision and accuracy when different number of images is retrieved. But the results obtained by ELM algorithm shows some more improvement than SVM. Such as when top 25 images are retrieved then in case of Brodatz database the precision is up to 94% and for MIT VisTex database its value is up to 96% with ELM classifier which is very much superior to other existing texture retrieval methods.

Automatic Land Cover Reconstruction From Historical Aerial Images: An Evaluation of Features Extraction and Classification Algorithms.

The land cover reconstruction from monochromatic historical aerial images is a challenging task that has recently attracted an increasing interest from the scientific community with the proliferation of large-scale epidemiological studies involving retrospective analysis of spatial patterns. However, the efforts made by the computer vision community in remote-sensing applications are mostly focused on prospective approaches through the analysis of high-resolution multi-spectral data acquired by the advanced spatial programs. Hence, four contributions are proposed in this paper. They aim at providing a comparison basis for the future development of computer vision algorithms applied to the automation of the land cover reconstruction from monochromatic historical aerial images. First, a new multi-scale multi-date dataset composed of 4.9 million non-overlapping annotated patches of the France territory between 1970 and 1990 has been created with the help of geography experts. This dataset has been named HistAerial. Second, an extensive comparison study of the state-of-the-art texture features extraction and classification algorithms, including deep convolutional neural networks (DCNNs), has been performed. It is presented in the form of an evaluation. Third, a novel low-dimensional local texture filter named rotated-corner local binary pattern (R-CRLBP) is presented as a simplification of the binary gradient contours filter through the use of an orthogonal combination representation. Finally, a novel combination of low-dimensional texture descriptors, including the R-CRLBP filter, is introduced as a light combination of local binary patterns (LCoLBPs). The LCoLBP filter achieved state-of-the-art results on the HistAerial dataset while conserving a relatively low-dimensional feature vector space compared with the DCNN approaches (17 times shorter).

Symbolic Aggregate approXimation-Local Binary Pattern Feature Descriptor Combination for Automatic Facial Expression Recognition

Automatic identification of facial expression is a significant research area which is anticipated for real time processing in Human-Computer Interaction domain. Along with an efficient classifier for assigning the class label to each of the input face image, it is very necessary to have a strong feature vector for training the classifier. This paper proposes an effectual combination of Local Binary Pattern and Symbolic Aggregate approXimation method for the feature vector generation for the classifier. Twenty one facial patches are extracted from the face image and the LBP value and SAX string for these twenty one patches are utilised for feature vector generation. The feature vectors of images are submitted to the Ensemble Bag classifier for training purpose. Images which were not used for training is used for testing. An average accuracy of 98.7% was obtained when tested on JAFFE data set for seven expressions and an accuracy of 96.96% was obtained for nine expressions on fused database. A detailed analysis of the testing conducted on images with partial occlusion and illumination variance are presented here.

Local line directional neighborhood pattern for texture classification

Local binary pattern (LBP) and combination of LBPs have shown to be a powerful and effective descriptor for texture analysis. In this paper, a novel approach to pattern recognition problem namely local line directional neighborhood pattern (LLDNP) for texture classification is proposed. The proposed LLDNP extracts the directional edge information of an image at 0°, 15°, 30°, 45°, 60°, 75°, 90°, 105°, 120°, 135°, 150°, and 165°. The sign and magnitude patterns are computed using the neighborhood pixel values in all directions. The sign pattern provides the local information of an image and is computed by comparing the neighborhood pixels. The magnitude pattern also provides additional information on images. The performance of the proposed method is compared with other existing methods by conducting experiments on five benchmark databases namely Brodatz, Outex, CUReT, UIUC, and Virus. The experimental results prove that the performance of the proposed method has achieved higher retrieval rate than other existing approaches.

Gender Classification Based on Multiscale Facial Fusion Feature

For gender classification, we present a new approach based on Multiscale facial fusion feature (MS3F) to classify gender from face images. Fusion feature is extracted by the combination of Local Binary Pattern (LBP) and Local Phase Quantization (LPQ) descriptors, and a multiscale feature is generated through Multiblock (MB) and Multilevel (ML) methods. Support Vector Machine (SVM) is employed as the classifier to conduct gender classification. All the experiments are performed based on the Images of Groups (IoG) dataset. The results demonstrate that the application of Multiscale fusion feature greatly improves the performance of gender classification, and our approach outperforms the state-of-the-art techniques.

Improved Human Detection with a Fusion of Laser Scanner and Vision/Infrared Information for Mobile Applications

This paper presents a method for human detection using a laser scanner with vision or infrared images. Mobile applications require reliable and efficient methods for human detection, especially as a part of driver assistance systems, including pedestrian collision systems. The authors propose an efficient method for multimodal human detection based on a combination of the features and context information. Strictly, the human is detected in the vision/infrared images using a combination of local binary patterns and histogram of oriented gradients features with a neural network in a cascade manner. Next, using coordinates of detected humans from the vision system, the moving trajectory is predicted until the scanner working distance is reached by the individual human. Then the segmentation of data from the laser scanner is further carried out with respect to the predicted trajectory. Finally, human detection in the laser scanner working distance is performed based on modelling of the human legs. The modelling is based on the adaptive breakpoint detection algorithm and proposed improved polylines definition and fitting algorithm. The authors conducted a set of experiments in predefined scenarios, discussed the identified weakness and advantages of the proposed method, and outlined detailed future work, especially for night-time and low-light conditions.

Effective plant discrimination based on the combination of local binary pattern operators and multiclass support vector machine methods

Accurate crop and weed discrimination plays a critical role in addressing the challenges of weed management in agriculture. The use of herbicides is currently the most common approach to weed control. However, herbicide resistant plants have long been recognised as a major concern due to the excessive use of herbicides. Effective weed detection techniques can reduce the cost of weed management and improve crop quality and yield. A computationally efficient and robust plant classification algorithm is developed and applied to the classification of three crops: Brassica napus (canola), Zea mays (maize/corn), and radish. The developed algorithm is based on the combination of Local Binary Pattern (LBP) operators, for the extraction of crop leaf textural features and Support vector machine (SVM) method, for multiclass plant classification. This paper presents the first investigation of the accuracy of the combined LBP algorithms, trained using a large dataset of canola, radish and barley leaf images captured by a testing facility under simulated field conditions. The dataset has four subclasses, background, canola, corn, and radish, with 24,000 images used for training and 6000 images, for validation. The dataset is referred herein as “bccr-segset” and published online. In each subclass, plant images are collected at four crop growth stages. Experimentally, the algorithm demonstrates plant classification accuracy as high as 91.85%, for the four classes.

Enhancing color image retrieval performance with feature fusion and non-linear support vector machine classifier

The purpose of this work is twofold: A fusion framework is proposed wherein the color histogram (CH), orthogonal combination of local binary patterns (OC-LBP), and color difference histogram (CDH) features are exploited to capture color, texture and shape information of an image, and a detailed comparative analysis of classical distance measures with non-linear support vector machine classifier (SVM) is presented. The proposed fusion is compared with individual and other fused features such as CH, OC-LBP, CDH, OC-LBP + CH, CH + CDH, OC-LBP + CDH in the L*a*b* color space. Detailed experiments reveal that the non-linear SVM classifier with pre-computed square-chord kernel, when used with any feature, outperforms other kernels and classical measures in terms of recognition rate on five datasets: SIMPLIcity/Wang, OT-Scene, Corel-5K, Corel-10K, and UKbench. Further, the proposed fused features i.e. CH + OC-LBP + CDH using non-linear SVM classifier with pre-computed square-chord kernel gives the best accuracy for all the aforementioned datasets.

Classification of Coral Reef Submarine Images and Videos Using a Novel Z with Tilted Z Local Binary Pattern (Z⊕TZLBP)

In this paper, a novel feature descriptor named Z with Tilted Z Local Binary Pattern (ZźTZLBP) is proposed for extracting coral reef image features efficiently. The aim is to reduce LBP's computational complexity by reducing the size of the feature vector. This is achieved in the proposed ZźTZLBP by dividing the neighborhood pixels into two non overlapped groups of Z and TZ (Tilted Z), and computing LBP wherein the centre pixel is also treated as one of the neighbors. KNN classification with four different distance metrices has been used for classification purpose. Metric F-measure is used to evaluate the performance of the proposed system. Experiments conducted with various coral image and video data sets show that the proposed feature descriptor outperforms Local Binary Pattern (LBP), Uniform Pattern, Center-Symmetric Local Binary Pattern and Orthogonal Combination of Local Binary Pattern and also guarantees accurate and efficient feature extraction.

Comparative Analysis of Texture Classification Using Local Binary Pattern and Its Variants

Texture classification is an important issue in digital image processing and the Local Binary pattern (LBP) is a very powerful method used for analysing textures. LBP has gained significant popularity in texture analysis world. However, LBP method is very sensitive to noise and unable to capture the macrostructure information of the image. To address its limitation, some variants of LBP have been defined. In this chapter, the texture classification performance of LBP has been compared with the five latest high-performance LBP variants, like Centre symmetric Local Binary Pattern (CS-LBP), Orthogonal Combination of Local Binary Patterns (OC LBP), Rotation Invariant Local Binary Pattern (RLBP), Dominant Rotated Local Binary Pattern (DRLBP) and Median rotated extended local binary pattern (MRELBP). This was by using the standard images Outex_TC_0010 dataset. From the experimental results it is concluded that DRLBP and MRELBP are the best methods for texture classification.

Boosting local texture descriptors with Log-Gabor filters response for improved image retrieval

In the recent past, many local texture descriptors have been proposed for the image retrieval task. In order to improve the image retrieval accuracy, quite a few of these descriptors have been implemented on Gabor filter response. However, the response of Log-Gabor filters has been proved to be better than Gabor filters with respect to their discrimination ability. In this paper, we present a framework for image retrieval that applies various local texture descriptors on Log-Gabor filters response. To evaluate the retrieval performance of the proposed framework, experiments have been conducted on standard Wang, VisTex and OT-Scene databases. Consistent improvement in the image retrieval accuracy demonstrates the effectiveness of this framework. Further, the experimental results show that the use of proposed framework with low-dimension texture descriptors such as Orthogonal Combination of Local Binary Pattern makes them a better choice over Local Binary Pattern and its high-dimensional variants when higher retrieval accuracy, small feature vector size and ease of computation is desired.

Improved local ternary patterns for automatic target recognition in infrared imagery.

This paper presents an improved local ternary pattern (LTP) for automatic target recognition (ATR) in infrared imagery. Firstly, a robust LTP (RLTP) scheme is proposed to overcome the limitation of the original LTP for achieving the invariance with respect to the illumination transformation. Then, a soft concave-convex partition (SCCP) is introduced to add some flexibility to the original concave-convex partition (CCP) scheme. Referring to the orthogonal combination of local binary patterns (OC_LBP), the orthogonal combination of LTP (OC_LTP) is adopted to reduce the dimensionality of the LTP histogram. Further, a novel operator, called the soft concave-convex orthogonal combination of robust LTP (SCC_OC_RLTP), is proposed by combing RLTP, SCCP and OC_LTP Finally, the new operator is used for ATR along with a blocking schedule to improve its discriminability and a feature selection technique to enhance its efficiency Experimental results on infrared imagery show that the proposed features can achieve competitive ATR results compared with the state-of-the-art methods.

An Evolutive Approach for Smile Recognition in Video Sequences

Facial expression recognition is one of the most challenging research areas in the image recognition field and has been actively studied since the 70's. For instance, smile recognition has been studied due to the fact that it is considered an important facial expression in human communication, it is therefore likely useful for human–machine interaction. Moreover, if a smile can be detected and also its intensity estimated, it will raise the possibility of new applications in the future. We are talking about quantifying the emotion at low computation cost and high accuracy. For this aim, we have used a new support vector machine (SVM)-based approach that integrates a weighted combination of local binary patterns (LBPs)-and principal component analysis (PCA)-based approaches. Furthermore, we construct this smile detector considering the evolution of the emotion along its natural life cycle. As a consequence, we achieved both low computation cost and high performance with video sequences.

Face Recognition Based on LBP and Extreme Learning Machine

This paper proposes a face recognition algorithm based on the combination of local binary pattern (LBP) texture features and extreme learning machine (ELM). The face image is divided into several regions, and the LBP features are extracted from these regions and combined together to form a feature vector which will be the input data of ELM. It shows that ELM performs well in classification applications, and ELM and support vector machine (SVM) are equivalent from the optimization point of view. But ELM has milder optimization constraints and much less training time. Our experiments are carried out on two well-known face databases, and the results show that compared with compared to PCA+NN, PCA+SVM and PCA+ELM the proposed method can achieve higher recognition rates.

Image region description using orthogonal combination of local binary patterns enhanced with color information

Visual content description is a key issue for machine-based image analysis and understanding. A good visual descriptor should be both discriminative and computationally efficient while possessing some properties of robustness to viewpoint changes and lighting condition variations. In this paper, we propose a new operator called the orthogonal combination of local binary patterns (denoted as OC-LBP) and six new local descriptors based on OC-LBP enhanced with color information for image region description. The aim is to increase both discriminative power and photometric invariance properties of the original LBP operator while keeping its computational efficiency. The experiments in three different applications show that the proposed descriptors outperform the popular SIFT, CS-LBP, HOG and SURF, and achieve comparable or even better performances than the state-of-the-art color SIFT descriptors. Meanwhile, the proposed descriptors provide complementary information to color SIFT, because a fusion of these two kinds of descriptors is found to perform clearly better than either of the two separately. Moreover, the proposed descriptors are about four times faster to compute than color SIFT.