LBP Features Research Articles

Machine Learning based Application to detect Pepper Leaf Diseases using HistGradientBoosting Classifier with fused HOG and LBP Features

Machine Learning based Application to detect Pepper Leaf Diseases using HistGradientBoosting Classifier with fused HOG and LBP Features

Hybrid Approach to Content-Based Image Retrieval Using Modified Multi-Scale LBP and Color Features

The objective of the content-based image retrieval (CBIR) system is to retrieve the visually identical images from the database efficiently and effectively. It is a broad research realm with the availability of numerous applications. Performance dependence of CBIR focuses on the extraction, reduction, and selection of the features along with the practice of classification technique. In this work, we have proposed the hybrid approach of two different feature descriptors: global color histogram and multi-scale local binary pattern (MS-LBP); furthermore, the use of PCA for dimension reduction and LDA for the selection of features. The proposed method is evaluated concerning various benchmark datasets, viz., Corel-1k, Corel-5k, Corel-10k, and Ghim-10k together with result comparison based on the precision and recall values at different thresholds. The classification purposes are satisfied with Euclidean and City Block distance. The performance study of the proposed work displays it as outperformer than the identified state-of-the-art literature.

A Facial Expression Recongnition Method Based on Dlib, RI-LBP and ResNet

A method of facial expression recognition using a composite feature is proposed. The method combines the expanded Dlib facial feature detector, the rotation-invariant local binary pattern (RI-LBP) and the 50-layer ResNet neural network model (ResNet_50). First, the expanded Dlib was used to locate 83 feature points on the face, obtainting the Dlib feature after preprocessing and dimentionality reduction (PCA). Then, the rotation-invariant LBP feature was extracted from 8 important regions after tilt correction. Furthermore, a 50-layer ResNet neural network was used to extract the low level features from the images. Finally, the three features were combined and extreme learning machine (ELM) was used to classify the composite facial features. The experimental results on Jaffe and CK+ datasets showed that the proposed method performs better compared with other methods.

Faster R-CNN Network Based on Multi Feature Fusion for Efficient Face Detection

The putting into effect of deep learning gave all attention on deep convolutionary neural networks has gained great condition of having general approval in face discovery in near in time years. One of the still in the same way open tasks however is to make out small-scale points. The distance down of the convolutionary network can cause a quick becoming-smaller of the sent out point map for small faces, and most scale unchanging views can hardly grip less than 15×15 bit of picture faces. There are few types of Haar-like rectangle points, which send in to the hard question that the training time for the classifier is too long because of, in relation to the greatly sized number of point amounts needed to put in order the small faces. In order to get answer to this hard question, we offer the MB-LBP (Multi-scale Local based on good example) features and joined rotation-invariant LBP (nearby based on good example) features based on the quicker R-CNN classifier called convolutional neural network (put) in middle in the gave greater value to quicker field, range (EFR-CNN). Despite the shortcomings of MB-LBP point and rotation-invariant LBP point on face edge knowledge, the canny operator-based edge azimuth field purpose, use is grouped together with the above features to make statement of the sense of words of the face news given. In place, based on the grouped together point group, a given to overmuch pleasure R-CNN network of parallel form is suggested. The proposals are put on one side to three being like (in some way) given to overmuch pleasure R-CNN networks according to the different rates on a hundred that they cover on the pictures. The three networks are separated by the measures of the map and (make, become, be) different in the weight of the concatenation 8 of purpose, use maps from one another. The offered EFR-CNN system gets done giving undertaking operation on common points of comparison, including FDDB, AFW, PASCAL faces, And wider face, made a comparison of with state-of – the-art face discovery methods such as Unit Box, hyperface Fastcnn.

Faster R-CNN Network Based on Multi Feature Fusion for Efficient Face Detection

The putting into effect of deep learning gave all attention on deep convolutionary neural networks has gained great condition of having general approval in face discovery in near in time years. One of the still in the same way open tasks however is to make out small-scale points. The distance down of the convolutionary network can cause a quick becoming-smaller of the sent out point map for small faces, and most scale unchanging views can hardly grip less than 15x15 bit of picture faces. There are few types of Haar-like rectangle points, which send in to the hard question that the training time for the classifier is too long because of, in relation to the greatly sized number of point amounts needed to put in order the small faces. In order to get answer to this hard question, we offer the MBLBP (Multi-scale Local based on good example) features and joined rotation-invariant LBP (nearby based on good example) features based on the quicker R-CNN classifier called convolutional neural network (put) in middle in the gave greater value to quicker field, range (EFR-CNN). Despite the shortcomings of MB-LBP point and rotation-invariant LBP point on face edge knowledge, the canny operator-based edge azimuth field purpose, use is grouped together with the above features to make statement of the sense of words of the face news given. In place, based on the grouped together point group, a given to overmuch pleasure R-CNN network of parallel form is suggested. The proposals are put on one side to three being like (in some way) given to overmuch pleasure R-CNN networks according to the different rates on a hundred that they cover on the pictures. The three networks are separated by the measures of the map and (make, become, be) different in the weight of the concatenation 8 of purpose, use maps from one another. The offered EFR-CNN system gets done giving undertaking operation on common points of comparison, including FDDB, AFW, PASCAL faces, And wider face, made a comparison of with stateof - the-art face discovery methods such as UnitBox, hyperface Fastcnn.

Thresholding binary coding for image forensics of weak sharpening

Image forensics of sharpening has aroused the great interest of researchers in recent decades. The state-of-the-art techniques have achieved high accuracies of strong sharpening detection, while it remains a challenge to detect weak sharpening. This paper proposes an algorithm based on thresholding binary coding for image sharpening detection. The overshoot artifact introduced by sharpening enlarges the difference between the local maximum and minimum of both image pixels and unsharp mask elements, based on which the threshold local binary pattern operator is applied to capture the trace of sharpening. Then the patterns are coded according to the rotation symmetry invariance and the texture type. Features are extracted from the statistical distribution of the coded patterns and fed to the classifier for sharpening detection. In practice, two classifiers are constructed for the lightweight and offline applications respectively, one is a single Fisher linear discriminant (FLD) with 182 features, and the other is an ensemble classifier (EC) with 5460 features. The experimental results on BOSS, NRCS and RAISE datasets show that for weak sharpening detection, the FLD outperforms the CNN and SVMs with EPTC, EPBC, and LBP features, and using EC with TBCs features further improves the performance, which obtains better results than ECs with TLBP and SRM features. Besides, the proposed algorithm is robust to post-JPEG compression and noise addition and could differentiate sharpening from other manipulations.

Fine-Grained Facial Expression Recognition in the Wild

Over the past decades, researches on facial expression recognition have been restricted within six basic expressions (anger, fear, disgust, happiness, sadness and surprise). However, these six words can not fully describe the richness and diversity of human beings’ emotions. To enhance the recognitive capabilities for computers, in this paper, we focus on fine-grained facial expression recognition in the wild and build a brand new benchmark FG-Emotions to push the research frontiers on this topic, which extends the original six classes to more elaborate thirty-three classes. Our FG-Emotions contains 10,371 images and 1,491 video clips annotated with corresponding fine-grained facial expression categories and landmarks. FG-Emotions also provides several features ( e.g. , LBP features and dense trajectories features) to facilitate related research. Moreover, on top of FG-Emotions , we propose a new end-to-end Multi-Scale Action Unit (AU)-based Network (MSAU-Net) for facial expression recognition with image which learns a more powerful facial representation by directly focusing on locating facial action units and utilizing “zoom in” operation to aggregate distinctive local features. As for recognition with video, we further extend the MSAU-Net to a two-stream model (TMSAU-Net) by adding a module with attention mechanism and a temporal stream branch to jointly learn spatial and temporal features. (T)MSAU-Net consistently outperforms existing state-of-the-art solutions on our FG-Emotions and several other datasets, and serves as a strong baseline to drive the future research towards fine-grained facial expression recognition in the wild.

Branch-structured detector for fast face detection using asymmetric LBP features

Face detection has been widely used to solve many important problems. In this paper, we present a fast face detector that is more suitable to be used by a low-cost embedded system. It can obtain high performance but use less CPU and memory resources. We first present a kind of pixel-level image feature named asymmetric LBP feature (ALBP). Furthermore, we find four kinds of four-bit ALBP features that are suitable to be used to construct cascade classifiers, since the four-bit ALBP features are discriminative features and large feature pools can be generated even if a small image patch is given. Second, we propose a branch-structured face detector that is composed of three ALBP-based cascade classifiers, i.e., one deep ALBP cascade and two shallow ALBP cascades. The detection speed and performance on FDDB dataset of the branch-structured face detector have both been evaluated. Experimental results show that it runs much faster than existing face detectors run on similar platforms, and its performance is close to that of well-known non-CNN face detectors which can achieve high performance.

Recognition of Historical Handwritten Kannada Characters Using Local Binary Pattern Features

Archaeological departments throughout the world have undertaken massive digitization projects to digitize their historical document corpus. In order to provide worldwide visibility to these historical documents residing in the digital libraries, a character recognition system is an inevitable tool. Automatic character recognition is a challenging problem as it needs a cautious blend of enhancement, segmentation, feature extraction, and classification techniques. This work presents a novel holistic character recognition system for the digitized Estampages of Historical Handwritten Kannada Stone Inscriptions (EHHKSI) belonging to 11th century. First, the EHHKSI images are enhanced using Retinex and Morphological operations to remove the degradations. Second, the images are segmented into characters by connected component labeling. Third, LBP features are extracted from these characters. Finally, decision tree is used to learn these features and classify the characters into appropriate classes. The LBP features improved the performance of the system significantly.

Classification and recognition of computed tomography images using image reconstruction and information fusion methods

In this paper, we propose a diagnosis and classification method of hydrocephalus computed tomography (CT) images using deep learning and image reconstruction methods. The proposed method constructs pathological features differing from the other healthy tissues. This method tries to improve the accuracy of pathological images identification and diagnosis. Identification of pathological features from CT images is an essential subject for the diagnosis and treatment of diseases. However, it is difficult to accurately distinguish pathological features owing to the variability of appearances, fuzzy boundaries, heterogeneous densities, shapes and sizes of lesions, etc. Some study results reported that the ResNet network has a better classification and diagnosis performance than other methods, and it has broad application prospectives in the identification of CT images. We use an improved ResNet network as a classification model with our proposed image reconstruction and information fusion methods. First, we evaluate a classification experiment using the hydrocephalus CT image datasets. Through the comparative experiments, we found that gradient features play an important role in the classification of hydrocephalus CT images. The classification effect of CT images with small information entropy is excellent in the evaluation of hydrocephalus CT images. A reconstructed image containing two channels of gradient features and one channel of LBP features is very effective in classification. Second, we apply our proposed method in classification experiments on CT images of colonography polyps for an evaluation. The experimental results have consistency with the hydrocephalus classification evaluation. It shows that the method is universal and suitable for classification of CT images in these two applications for the diagnosis of diseases. The original features of CT images are not ideal characteristics in classification, and the reconstructed image and information fusion methods have a great effect on CT images classification for pathological diagnosis.

Attention mechanism-based CNN for facial expression recognition

Facial expression recognition is a hot research topic and can be applied in many computer vision fields, such as human–computer interaction, affective computing and so on. In this paper, we propose a novel end-to-end network with attention mechanism for automatic facial expression recognition. The new network architecture consists of four parts, i.e., the feature extraction module, the attention module, the reconstruction module and the classification module. The LBP features extract image texture information and then catch the small movements of the faces, which can improve the network performance. Attention mechanism can make the neural network pay more attention to useful features. We combine LBP features and attention mechanism to enhance the attention model to obtain better results. In addition, we collected and labelled a new facial expression dataset of seven expressions from 35 subjects aged from 20 to 25. For each subject, we captured both RGB images and depth images with a Microsoft Kinect sensor. For each image type, there are 245 image sequences, each of which contains 110 images, resulting in 26,950 images in total. We apply the newly proposed method to our own dataset and four representative expression datasets, i.e., JAFFE, CK+, FER2013 and Oulu-CASIA. The experimental results demonstrate the feasibility and effectiveness of the proposed method.

Internal crack detection of castings: a study based on relief algorithm and Adaboost-SVM

The internal crack defect is prone to appear in the inner part of casting products during the production process due to the influence of casting craft and on-site environment. In order to ensure the internal quality of the casting products, nondestructive examination and defect detection technology should be used to detect the internal crack of the casting product. The existing defect detection technologies have some problems such as poor generalization and low accuracy. Therefore, an internal crack defect detection method based on the Relief algorithm and Adaboost-SVM is proposed in this paper. Firstly, casting image is preprocessed by grayscale transformation, bilateral filtering, and adaptive image segmentation. Secondly, HOG feature, invariant moment feature, and LBP feature are extracted, and sensitive feature set is selected by Relief algorithm. Finally, the Adaboost-SVM is used to construct the internal crack detection model to realize the crack detection with high generalization and accuracy. The effectiveness of the method is verified by the casting image dataset collected in the actual industrial field. The experimental result reveals that the proposed method could not only extract sensitive feature set but also has better classification performance and generalization ability than other common classifiers.

Violent Video Event Detection Based on Integrated LBP and GLCM Texture Features

Violent Video Event Detection Based on Integrated LBP and GLCM Texture Features

Review on Medical Image Retrieval Based on Wavelet, Bag of Features and Relevance Feedback

Technological advances have evolved in all the directions including the biomedical, because of which a record number of lives are saved every day. The advancement has now surpassed the tools level, now the doctors with the help of new tools can also detect diseases, which saves the response time. In this paper, we will work on one such technique which will help in retrieving the similar type of images with the help of their features. In this paper, the features such as Texture features, LBP features, Retrieval feature, which are processed with hash coding and relevance feedback to get the final results. The framework provides the output utilizing a hash coding classifiers which predict the image from the database of the images. The images are classified on a global level with the help of multiple low-level features.

An Optimized Approach for Breast Cancer Classification for Histopathological Images Based on Hybrid Feature Set.

Breast cancer is considered as one of the most perilous sickness among females worldwide and the ratio of new cases is increasing yearly. Many researchers have proposed efficient algorithms to diagnose breast cancer at early stages, which have increased the efficiency and performance by utilizing the learned features of gold standard histopathological images. Most of these systems have either used traditional handcrafted or deep features, which had a lot of noise and redundancy, and ultimately decrease the performance of the system. A hybrid approach is proposed by fusing and optimizing the properties of handcrafted and deep features to classify the breast cancer images. HOG and LBP features are serially fused with pre-trained models VGG19 and InceptionV3. PCR and ICR are used to evaluate the classification performance of the proposed method. The method concentrates on histopathological images to classify the breast cancer. The performance is compared with the state-of-the-art techniques, where an overall patient-level accuracy of 97.2% and image-level accuracy of 96.7% is recorded. The proposed hybrid method achieves the best performance as compared to previous methods and it can be used for the intelligent healthcare systems and early breast cancer detection.

Blotch Detection in Archive Films Based on Visual Saliency Map

Degradations frequently occur in archive films that symbolize the historical and cultural heritage of a nation. In this study, the problem of detection blotches commonly encountered in archive films is handled. Here, a block-based blotch detection method is proposed based on a visual saliency map. The visual saliency map reveals prominent areas in an input frame and thus enables more accurate results in the blotch detection. A simple and effective visual saliency map method is taken into consideration in order to reduce computational complexity for the detection phase. After the visual saliency maps of the given frames are obtained, blotch regions are estimated by considered spatiotemporal patches—without the requirement for motion estimation—around the saliency pixels, which are subjected to a prethresholding process. Experimental results show that the proposed block-based blotch detection method provides a significant advantage with reducing false alarm rates over HOG feature (Yous and Serir, 2017), LBP feature (Yous and Serir, 2017), and regions-matching (Yous and Serir, 2016) methods presented in recent years.

A deep neural network and classical features based scheme for objects recognition: an application for machine inspection

Computer Vision (CV) domain is widely used in the current era of automation and visual surveillance for the detection and classification of different objects in a diverse environment. The automatic machine inspection of different objects in the scenes is based on internal and external parameters like features that provide a huge amount of information related to the nature of an object in the scene. In this work, we propose a new automated method based on classical and deep learning feature selection. The proposed object classification method follows three steps. The data augmentation is performed in the first step to make the balance database. Later, Pyramid HOG (PHOG) and Central Symmetric LBP (CS-LBP) features are serially fused along with deep learning-based extracted features. The deep learning features are extracted from the pre-trained CNN model name Inception V3. In the third step, a new technique name Joint Entropy along with KNN (JEKNN) is employed to select the best features. The best-selected features are finally classified by well-known supervised learning methods and choose the best one based on higher accuracy. The proposed method is evaluated on Caltech101 balanced dataset and achieved maximum accuracy of 90.4% on Ensemble classifier which outperforms as compare to existing techniques.

Detection of Late Blight in Potato Leaves Based on Multi-Feature and SVM Classifier

Due to the influence of germs and viruses, plants often show various symptoms of diseases and insect pests during the growth process, which leads to a large economic loss of fruit farmers. It also brings a certain economic loss to our society, so prevent earlier and advise growers about plant diseases and insect pests have important value and significance. In this case, this paper proposes a detection method which is based on the combination of HOG, LBP and CSS features with Support Vector Machine (SVM) classifier. This method extracts the histogram of oriented gradients, texture, and color self-similar features of potato leaves, and then training samples with SVM classifier to detect late blight as early as possible in the early stages of potato growth. In addition, this paper proposes a method to increase virtual samples, that is, generating symmetrical samples according to the original samples. Due to the limitation of the number of collected samples, increasing symmetrical samples can expand the diversity of samples. The results show that this method can obtain a detection rate of 92.7%, and has better detection and recognition performance in practical application.

Local binary pattern-based on-road vehicle detection in urban traffic scene

For intelligent traffic monitoring systems and related applications, detecting vehicles on roads is a vital step. However, robust and efficient vehicles detection is still a challenging problem due to variations in the appearance of the vehicles and complicated background of the roads. In this paper, we propose a simple and effective vehicle detection method based on local vehicle's texture and appearance histograms feed into clustering forests. The interdependency of vehicle's parts locations is incorporating within a clustering forests framework. Local binary pattern-like descriptors are utilized for texture feature extraction. Through utilizing the LBP descriptors, the local structures of vehicles, such as edge, contour and flat region can be effectively depicted. The align set of histograms generated concurrence with LBPs spatial for random sampled local regions are used to measure the dissimilarity between regions of all training images. Evaluating the fit between histograms is built in clustering forests. That is, clustering discriminative codebooks of latent features are used to search between different LBP features of the random regions utilizing the Chi-square dissimilarity measure. Besides, saliency maps built by the learnt latent features are adopted to determine the vehicles locations in test image. Effectiveness of the proposed method is evaluated on different car datasets stressing various imaging conditions and the obtained results show that the method achieves significant improvements compared to published methods.

Texture Feature Extraction and Classification of Brain Neoplasm in MR Images using Machine Learning Techniques

Identification of pathology in brain such as tumor lesions is a tedious task. MRI is most of the time chosen medical imaging procedure that often pacts with lenient tissues such as brain tissues, tendons and ligaments. This study aims at texture feature extraction and segregation of brain tumor cases into benign and malignant conditions. The stages involved are segmentation, feature extraction and classification. K-means clustering method is preferred for segmentation and selecting the required region of interest. The textural information is captured from region of interest using GLCM, HOG and LBP patterns. ANN, SVM and k-NN classifiers are used to analyze performance accuracy in classifying the tumor data into benign and malignant conditions in brain MR images. ANN with LM training algorithm provides high accuracy with best performance compared to other two classifiers in identifying benign and malignant conditions of tumors by using a combination of GLCM, LBP and HOG feature extraction process successfully. The recommended method is compared with few current approaches in terms of feature extraction and classification.