Histogram Of Oriented Gradients Features Research Articles

Feature extraction and classification of dance movements based on data mining

Abstract In recent years, the expansive utilization of data mining techniques has revolutionized various fields, including the analysis of dance videos. This study leverages data mining to meticulously capture and analyze dance movements, thereby facilitating the enhancement and correction of dancers’ techniques. Within the scope of this research, images of dance movements extracted from videos are subjected to preprocessing, which involves grayscaling and thresholding, to prepare them for further analysis. Building on these processed images, this paper introduces a novel multi-feature dance action recognition approach. This method integrates several distinct features—directional gradient histogram features, optical flow directional histogram features, and audio features—employing a linear weighting scheme within a multi-core learning framework. The efficacy of the proposed approach is demonstrated through its performance on the FolkDance dance dataset, where it achieves a 3.5% increase in fusion accuracy over the traditional Dance Style Identification (DSI) method. Additionally, when compared with the Multi-Feature Learning-Combined (MFL-C) method, our approach shows an improvement of 0.6% in fusion accuracy. This research establishes a viable method for the recognition and classification of dance movements, laying a robust foundation for further inquiry and practical applications in this domain.

Comparative analysis of mature tomato detection by feature extraction and machine learning for autonomous greenhouse robots

Accurate detection of tomatoes grown in greenhouses is important for timely harvesting. In this way, it is ensured that mature tomatoes are collected by distinguishing them from the unripe ones. Insufficient light, occlusion, and overlapping adversely affect the detection of mature tomatoes. In addition, it is time consuming for people to detect mature tomatoes at certain periods in large greenhouses. For these reasons, high-performance automatic detection of tomatoes by greenhouse robots has become an increasingly studied area today. In this paper, two feature extraction methods, histogram of oriented gradients (HOG) and local binary patterns (LBP), which are effective in object recognition, and two important and commonly used classifiers of machine learning, support vector machines (SVM) and k-nearest neighbor (kNN), are comparatively used to detect and count tomatoes. The HOG and LBP features are classified separately and together by SVM or kNN, and the success of each case are compared. Performance of the detection is improved by eliminating false positive results at the postprocessing stage using color information.

Multi-Method Analysis of Histopathological Image for Early Diagnosis of Oral Squamous Cell Carcinoma Using Deep Learning and Hybrid Techniques.

Oral cancer is a fatal disease and ranks seventh among the most common cancers throughout the whole globe. Oral cancer is a type of cancer that usually affects the head and neck. The current gold standard for diagnosis is histopathological investigation, however, the conventional approach is time-consuming and requires professional interpretation. Therefore, early diagnosis of Oral Squamous Cell Carcinoma (OSCC) is crucial for successful therapy, reducing the risk of mortality and morbidity, while improving the patient's chances of survival. Thus, we employed several artificial intelligence techniques to aid clinicians or physicians, thereby significantly reducing the workload of pathologists. This study aimed to develop hybrid methodologies based on fused features to generate better results for early diagnosis of OSCC. This study employed three different strategies, each using five distinct models. The first strategy is transfer learning using the Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201 models. The second strategy involves using a pre-trained art of CNN for feature extraction coupled with a Support Vector Machine (SVM) for classification. In particular, features were extracted using various pre-trained models, namely Xception, Inceptionv3, InceptionResNetV2, NASNetLarge, and DenseNet201, and were subsequently applied to the SVM algorithm to evaluate the classification accuracy. The final strategy employs a cutting-edge hybrid feature fusion technique, utilizing an art-of-CNN model to extract the deep features of the aforementioned models. These deep features underwent dimensionality reduction through principal component analysis (PCA). Subsequently, low-dimensionality features are combined with shape, color, and texture features extracted using a gray-level co-occurrence matrix (GLCM), Histogram of Oriented Gradient (HOG), and Local Binary Pattern (LBP) methods. Hybrid feature fusion was incorporated into the SVM to enhance the classification performance. The proposed system achieved promising results for rapid diagnosis of OSCC using histological images. The accuracy, precision, sensitivity, specificity, F-1 score, and area under the curve (AUC) of the support vector machine (SVM) algorithm based on the hybrid feature fusion of DenseNet201 with GLCM, HOG, and LBP features were 97.00%, 96.77%, 90.90%, 98.92%, 93.74%, and 96.80%, respectively.

A novel fast hybrid face recognition approach using convolutional Kernel extreme learning machine with HOG feature extractor

The ability to identify a person's face from a digitized photo or video frame against a database of faces is known as facial recognition. In the past few years, algorithms that use deep learning to recognize faces have become more popular. The majority of them are predicated on extremely accurate but complicated Convolutional Neural Networks (CNNs), which require a lot of computational power, storage space, and a number of training epochs before they provide satisfying results, and are notably difficult to implement. In an effort to reduce the training time by reducing the number of epochs and increase accuracy, this paper introduces a novel fast hybrid face recognition approach HOG-CKELM, based on CNN that makes use of Kernel based Extreme Learning Machines (KELM) and Histogram of Oriented Gradients (HOG) as facial feature extractor. The effectiveness of the proposed hybrid face recognition technique is evaluated using AT & T, Yale, and JAFFE datasets. When compared to traditional HOG-CNN based techniques, the experimental evaluation indicates that the proposed method for face recognition is capable of achieving excellent performance in terms of accuracy and training time.

Utilization of information from CNN feature maps for offline word-level writer identification

This paper proposes a text-independent method for authorship identification using handwritten word images. Our method is text-independent and imposes no limitations on the size of the input word images being analyzed. To begin with, the SIFT algorithm is utilized to extract key-point regions (fragments) across different levels of abstraction, encompassing allographs, characters, or character combinations. These fragments are subsequently processed through a CNN network, yielding feature maps corresponding to convolution layers. The information in these maps is then transformed into a fixed-dimension representation using a modified version of the HOG feature descriptor.The noteworthy contribution of our proposal lies in harnessing additional cues from CNN’s feature maps for writer identification. We propose a measure to gauge the importance or ‘saliency’ of feature maps within a CNN layer during training. This measure originates from applying Sparse Principal Component Analysis (SPCA) to Histogram Of Gradient (HOG) features. Once saliency values are obtained, we combine them with HOG representations to create writer descriptors tailored to a CNN layer. The derived descriptors are then passed through a set of SVM classifiers that are scored at two levels to determine the identity of the handwritten word image.The efficacy of our system has been demonstrated on the word-level data of the CVL, IAM and CERUG-EN datasets with an accuracy of 82.10%, 87.68%, and 75.80% respectively. The results obtained are shown to be promising when compared with previous works.

Establishing a machine learning model for predicting nutritional risk through facial feature recognition.

Malnutrition affects many worldwide, necessitating accurate and timely nutritional risk assessment. This study aims to develop and validate a machine learning model using facial feature recognition for predicting nutritional risk. This innovative approach seeks to offer a non-invasive, efficient method for early identification and intervention, ultimately improving health outcomes. We gathered medical examination data and facial images from 949 patients across multiple hospitals to predict nutritional status. In this multicenter investigation, facial images underwent preprocessing via face alignment and cropping. Orbital fat pads were isolated using the U-net model, with the histogram of oriented gradient (HOG) method employed for feature extraction. Standardized HOG features were subjected to principal component analysis (PCA) for dimensionality reduction. A support vector machine (SVM) classification model was utilized for NRS-2002 detection. Our approach established a non-linear mapping between facial features and NRS-2002 nutritional risk scores, providing an innovative method for evaluating patient nutritional status. In context of orbital fat pad area segmentation with U-net model, the averaged dice coefficient is 88.3%. Our experimental results show that the proposed method to predict NRS-2002 scores achieves an accuracy of 73.1%. We also grouped the samples by gender, age, and the location of the hospital where the data were collected to evaluate the classification accuracy in different subsets. The classification accuracy rate for the elderly group was 85%, while the non-elderly group exhibited a classification accuracy rate of 71.1%; Furthermore, the classification accuracy rate for males and females were 69.2 and 78.6%, respectively. Hospitals located in remote areas, such as Tibet and Yunnan, yielded a classification accuracy rate of 76.5% for collected patient samples, whereas hospitals in non-remote areas achieved a classification accuracy rate of 71.1%. The attained accuracy rate of 73.1% holds significant implications for the feasibility of the method. While not impeccable, this level of accuracy highlights the potential for further improvements. The development of this algorithm has the potential to revolutionize nutritional risk assessment by providing healthcare professionals and individuals with a non-invasive, cost-effective, and easily accessible tool.

Face and Facial Expressions Recognition System for Blind People Using ResNet50 Architecture and CNN

Many blind individuals have difficulties in recognizing people’s facial expression which may impact their social interaction. With the recognition, the blind individuals can accurately interpret and respond to the emotions. There is a lack in the existing application with the combination of face and facial expressions recognition. The blind individuals have to rely on multiple applications to accomplish the same task, making it difficult and time-consuming for them to use. The paper aims to recognize faces and facial expressions for blind individuals and provides feedback in real-time. Three face detection algorithms of Haar Cascade Classifier, Dlib, and RetinaFace are compared. Dlib is chosen to process with Histogram of Oriented Gradients (HOG) and Support Vector Machine (SVM). It loads the pre-trained model, computes the HOG features, slide the window scanning at different scales, classify the windows using the SVM classifier, generate bounding boxes, and applying non-maximum suppression. ResNet50 architecture is employed to recognize face and Convolutional Neural Networks (CNN) is applied to recognize facial expression. The training accuracy is 70% and validation accuracy is 60%.

Two-Level Feature-Fusion Ship Recognition Strategy Combining HOG Features with Dual-Polarized Data in SAR Images

Due to the inherent characteristics of synthetic aperture radar (SAR) imaging, SAR ship features are not obvious and the category distribution is unbalanced, which makes the task of ship recognition in SAR images quite challenging. To address the above problems, a two-level feature-fusion ship recognition strategy combining the histogram of oriented gradients (HOG) features with the dual-polarized data in the SAR images is proposed. The proposed strategy comprehensively utilizes the features extracted by the HOG operator and the shallow and deep features extracted by the Siamese network in the dual-polarized SAR ship images, which can increase the amount of information for the model learning. First, the Siamese network is used to extract the shallow and deep features from the dual-polarized SAR images, and then the HOG feature of the dual-polarized SAR images is also extracted. Furthermore, the bilinear transformation layer is used for fusing the HOG features from dual-polarized SAR images, and the grouping bilinear pooling process is used for fusing the dual-polarized shallow feature and deep feature extracted by the Siamese network, respectively. Finally, the catenation operation is used for fusing the dual-polarized HOG features and dual-polarized shallow feature and deep feature, respectively, which are used for the recognition of the SAR ship targets. Experimental results tested on the OpenSARShip2.0 dataset demonstrate the correctness and effectiveness of the proposed strategy, which can effectively improve the recognition performance of the ship targets by fusing the different level features of the dual-polarized SAR images.

Performance Analysis of Naive Bayes Variation Method in Spice Image Classification Using Histogram of Gradient Oriented (HOG) Feature Extraction

Indonesia has a lot of natural wealth of spices. The diversity of spices is an inseparable aspect of Indonesian history. Spices and seasonings are biological resources that have long played an important role in human life. Indonesian spices have almost the same color and shape. The purpose of this study was to analyze the performance of the Naïve Bayes variation method in classifying spices using a Histogram Of Oriented Gradient (HOG) feature extraction. Based on 3 tests, the performance of the four Naïve Bayes variation methods carried out in this study, it can be seen that testing 5 types of spices using the Gaussian Naïve Bayes method obtained the best performance with an accuracy of 0.946, a precision of 0.95, a recall of 0.945, f1 score of 0.947, f beta score of 0.946, and Jaccard score of 0.90. Where as using the Complement Naïve Bayes method gets the lowest performance. From the results of this study it can be concluded that by utilizing HOG feature extraction and the Naïve Bayes variation method, maximum classification results are obtained in classifying spices. To obtain more accurate classification results, consider using other methods and other feature extraction

A computer-aided diagnosis system for brain tumors based on artificial intelligence algorithms.

The choice of treatment and prognosis evaluation depend on the accurate early diagnosis of brain tumors. Many brain tumors go undiagnosed or are overlooked by clinicians as a result of the challenges associated with manually evaluating magnetic resonance imaging (MRI) images in clinical practice. In this study, we built a computer-aided diagnosis (CAD) system for glioma detection, grading, segmentation, and knowledge discovery based on artificial intelligence algorithms. Neuroimages are specifically represented using a type of visual feature known as the histogram of gradients (HOG). Then, through a two-level classification framework, the HOG features are employed to distinguish between healthy controls and patients, or between different glioma grades. This CAD system also offers tumor visualization using a semi-automatic segmentation tool for better patient management and treatment monitoring. Finally, a knowledge base is created to offer additional advice for the diagnosis of brain tumors. Based on our proposed two-level classification framework, we train models for glioma detection and grading, achieving area under curve (AUC) of 0.921 and 0.806, respectively. Different from other systems, we integrate these diagnostic tools with a web-based interface, which provides the flexibility for system deployment.

Hybrid ICHO-HSDC Model For Accurate Covid-19 Detection and Classification From CT Scan And X-Ray Images

The worldwide demand for medical care has increased due to the increasing expansion of Covid-19 cases. Therefore, in this case, prompt and precise identification of this illness is crucial. Health professionals are using additional screening techniques including CT imaging as well as chest Xrays for this. Pre-processing the CT scan pictures to eliminate the areas of areas, normalize image contrast, and minimize image noise, however, receives little attention. The seriousness of the Covid- 19 infection must be assessed in addition to the Covid-19 detection and categorization. An ICHOHYBRID model for Covid-19 identification and classification from X-ray, as well as CT scan images, is offered as a solution to these issues. Histogram and morphological image processing methods are used for CT-scan images. The Improved Chicken Swarm Optimization (ICHO) technique is used to find the input image’s histogram threshold. The extracted areas are categorized using the Convolutional Neural Network method based on a feature vector. When infections are found, the CNN algorithm is used to categorize them as severe, moderate, or extremely severe using Support Vector Machine. To eliminate the noise from the test pictures for X-ray imaging, the Adapted Anisotropic Diffusion Filtering (A2DF) approach is used. Once the preprocessing is completed, features are extracted using an Image profile (IP) and Histogram-oriented gradient (HOG) to create a fused HOG and IP feature. Using the HYBRID method, the FHI characteristics are divided into 3 classes. When compared to SVM and CNN, the study provides the best accuracy, with scores of 94.6 for CT scan pictures and 95.6 for X-ray images.

Comparison of Support Vector Machine (SVM), K-Nearest Neighbor (K-NN), and Stochastic Gradient Descent (SGD) for Classifying Corn Leaf Disease based on Histogram of Oriented Gradients (HOG) Feature Extraction

Image classification involves categorizing an image's pixels into specific classes based on their unique characteristics. It has diverse applications in everyday life. One such application is the classification of diseases on corn leaves. Corn is a widely consumed staple food in Indonesia, and healthy corn plants are crucial for meeting market demands. Currently, disease identification in corn plants relies on manual checks, which are time-consuming and less effective. This research aims to automate disease identification on corn leaves using the Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) with K=2, and Stochastic Gradient Descent (SGD) algorithms. The classification process utilizes the Histogram of Oriented Gradients (HOG) feature extraction method with a dataset of corn leaf images. The classification results achieved an accuracy of 71.44%, AUC of 79.16%, precision of 70.08%, recall of 71.44%, and f1 score of 67.11%. The highest accuracy was obtained by combining HOG feature extraction with the SGD algorithm.

A Framework for Recognition of Facial Expression Using HOG Features

Recognition of facial expressions has been an important topic of study over the last several decades, and despite the advancements that have been made, it is still difficult to do because of the significant intra-class diversity. The handcrafted feature is used in traditional methods to address this issue. This feature is then preceded by a classifier that is trained using a database of pictures or videos. The majority of these works do quite well on datasets of photographs that were recorded in a controlled environment. However, they do not perform as well on datasets that are more difficult to work with since they include greater image variance and partial faces. The Histogram of Oriented Gradient (HOG) descriptor is the foundation for the strategy that is suggested in this study. During the initial step of the procedure, the input picture is pre-processed in order to locate the datum region, which assists in the extraction of the most relevant characteristics. After that, the Random Forest (RF) algorithm was employed as a classifier for facial expressions. The Japanese Female Facial Emotions Database (JAFFE) is used to assess our technique. The experimental findings demonstrated that the suggested method is accurate and effective in identifying facial expressions.

Employing histogram of oriented gradient to enhance concrete crack detection performance with classification algorithm and Bayesian optimization

This paper develops a crack detection method for concrete bridges using a binary classification algorithm, which utilizes the histogram of oriented gradient (HOG) feature to effectively and efficiently capture the crack characteristic. The method is implemented in three steps: collecting images with and without cracks, HOG feature calculation as input variables, and crack detection with a binary classification algorithm. Moreover, the influences of different HOG parameters are investigated to better capture the cracks' characteristics. The classification algorithm is adopted as a support vector machine (SVM) model, and Bayesian optimization is employed to select hyper-parameters in the model training. The method is verified with available datasets of images with and without cracks from concrete bridge decks. The corresponding performance is compared to that of other methods, such as convolutional neural network (CNN), Adaboost, K-Nearest Neighbor (KNN), and Naïve Bayes, evidencing higher accuracy and computation efficiency.

Vibration data‐driven machine learning architecture for structural health monitoring of steel frame structures

AbstractA vibration data‐based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag‐of‐Features algorithm that extracts the speeded‐up robust features (SURF) from the time‐frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K‐means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k‐nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.

Compares the effectiveness of the bagging method in classifying spices using the histogram of oriented gradient feature extraction technique

Spice classification is a crucial task in the food industry to ensure food safety and quality. This study focuses on the classification of spices using the Histogram of Oriented Gradient (HoG) feature extraction method and bagging method. The objective of this research is to compare the performance of three different models of bagging method, including Bootstrap Aggregating (Bagging), Random Forests, and Extra Tree Classifier, in classifying spices. The evaluation metrics used in this research are Precision, Recall, F1-Score, F2-Score, Jaccard Score, and Accuracy. The results show that the Random Forest model achieved the best performance, with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.861, 0.8633, 0.8587, 0.8607, 0.7694, and 0.8733 respectively. On the other hand, the Extra Tree Classifier had the lowest performance with precision, recall, F1-score, F2-Score, Jaccard, and Accuracy values of 0.7034, 0.7958, 0.7037, 0.7047, 0.5635, and 0.72 respectively. Overall, the results indicate a fairly good success rate in classifying spices using the HoG feature extraction method and bagging method. However, further evaluation is needed to improve the accuracy of the classification results, such as increasing the number of training data or considering the use of other feature extraction methods. The findings of this research may have significant implications for the food industry in ensuring the quality and safety of food products.

Handwritten Digits Recognition Based on a Parallel Optoelectronic Time-Delay Reservoir Computing System

In this work, the performance of an optoelectronic time-delay reservoir computing system for performing a handwritten digit recognition task is numerically investigated, and a scheme to improve the recognition speed using multiple parallel reservoirs is proposed. By comparing four image injection methods based on a single time-delay reservoir, we find that when injecting the histograms of oriented gradient (HOG) features of the digit image, the accuracy rate (AR) is relatively high and is less affected by the offset phase. To improve the recognition speed, we construct a parallel time-delay reservoir system including multi-reservoirs, where each reservoir processes part of the HOG features of one image. Based on 6 parallel reservoirs with each reservoir possessing 100 virtual nodes, the AR can reach about 97.8%, and the reservoir processing speed can reach about 1 × 106 digits per second. Meanwhile, the parallel reservoir system shows strong robustness to the parameter mismatch between multi-reservoirs.

Tire appearance defect detection method via combining HOG and LBP features

Tire appearance defect detection based on machine vision is an effective technology to improve the tire production quality. The detection process can be completed by the way of non-destructive testing. Therefore, more and more researchers are paying attention to this technology. However, tires are characterized by single block colors and various defects. It is a great challenge to accurately detect tire appearance defects. To complete the task of detecting tire defects, this paper presents a novel tire appearance defect detection method via combining histogram of oriented gradients (HOG) and local binary pattern (LBP) features. First, we construct a tire image dataset to provide defective and normal tire images. Then, histogram of oriented gradients and local binary pattern features of tire images are, respectively, extracted and used to train the support vector machine (SVM) classifier. Finally, the support vector machine classifier calculates the prediction scores of the test images via combining the histogram of oriented gradients and local binary pattern features. These scores can be utilized to determine whether the test image is a defective or a normal tire image, and the goal of tire appearance defect detection is achieved. Conducted on the tire image dataset, our method has verified the effectiveness of detecting tire detects, and the mean accuracy is improved more than 1.6% than the algorithm that only uses the histogram of oriented gradients or local binary pattern feature. The experimental results demonstrate that the combination of HOG and LBP features can increase tire appearance defect detection accuracy.

Efficient Feature Extraction for Recognition of Human Emotions through Facial Expressions Using Image Processing Algorithms

Face emotion recognition is a challenging problem in computer vision that has been extensively studied in recent years. The project investigates the performance of Local Binary Pattern (LBP), Histogram of Oriented Gradients (HOG), K- Nearest Neighbour (KNN), and Support Vector Machine (SVM) for face emotion recognition. The aim of this study is to evaluate the performance of different combinations of these techniques and to identify the most effective approach for this task. To achieve this, we first collected a dataset of facial expressions that includes seven basic emotions such as happy, sad, angry, surprise, Neutral, fear, and disgust. We then extract LBP and HOG features from the facial images and then KNN and SVM classifiers to classify the emotions. We experimented with various combinations of LBP, HOG, KNN, and SVM and evaluated the performance of each approach using metrics such as accuracy, precision, recall, and F1 score. This study demonstrates the effectiveness of combining LBP and HOG features with KNN and SVM for face emotion recognition. Our results suggest that SVM is the most effective model for this task, when it is combined with HOG features and can further improve the system39;s performance. The model can be implemented using MATLAB and GUI Interface. These findings have important implications for the development of accurate and reliable face emotion recognition systems for various applications, including human-computer interaction, gaming, and healthcare.

Feature fusion based artificial neural network model for disease detection of bean leaves

<abstract> <p>Plant diseases reduce yield and quality in agricultural production by 20–40%. Leaf diseases cause 42% of agricultural production losses. Image processing techniques based on artificial neural networks are used for the non-destructive detection of leaf diseases on the plant. Since leaf diseases have a complex structure, it is necessary to increase the accuracy and generalizability of the developed machine learning models. In this study, an artificial neural network model for bean leaf disease detection was developed by fusing descriptive vectors obtained from bean leaves with HOG (Histogram Oriented Gradient) feature extraction and transfer learning feature extraction methods. The model using feature fusion has higher accuracy than only HOG feature extraction and only transfer learning feature extraction models. Also, the feature fusion model converged to the solution faster. Feature fusion model had 98.33, 98.40 and 99.24% accuracy in training, validation, and test datasets, respectively. The study shows that the proposed method can effectively capture interclass distinguishing features faster and more accurately.</p> </abstract>