Pyramid Histogram Of Gradients Research Articles

Pyram: a robust and attack-resistant perceptual image hashing using pyramid histogram of gradients

Pyram: a robust and attack-resistant perceptual image hashing using pyramid histogram of gradients

Effect of multi filters in glucoma detection using random forest classifier

Glaucoma is an eye disease that damages the optic nerve which connects the eye to the brain. When the fluid pressure inside the eye (intraocular pressure) increases, the optic nerve get impaired and has doubled the chance for diabetic patients resulting in irreversible loss of vision if not detected in early stages. In developing countries, due to the scarcity of ophthalmic experts and lab facilities, the needs for eye disease detecting automation system are increased without saying. The field of artificial intelligence is providing many solution's especially in health care domain. The proposed work generate models for recognizing the presence of glaucoma based on open access public dataset of retinal fundus images using machine learning algorithms with the help of image feature descriptors. It classifies the given retinal fundus image as normal or abnormal in two stages. Firstly it extracts image features using appropriate filters and then it is trained through tree based ensemble classifier to classify the given input image and then the same is tested to get the better accuracy performance. The above two steps are iterated by varying over the three effective filters like edge histogram, fuzzy color and texture histogram and pyramid histogram of gradients. The proposed experiment based on this approach reveals that the use of Edge histogram filter in combination with fuzzy color and texture histogram with Random forest classifier yields maximum accuracy of 80.43% and AUC 0.884. The results obtained by applying multi filters is better than that obtained by applying single filter.

Deep Learning and Feature Extraction for Covid 19 Diagnosis

Recently, medical images analysis is becoming the center of interest in the medical field, with the helpful opportunities offered by artificial intelligence, especially deep learning techniques. Computers are becoming more and more capable of learning how to be diagnosing certain medical pathologies and diseases. In this domain, deep learning is a major choice, more precisely Convolutional Neural Networks (CNN) due to its powerful performance with images classification. In this paper, a new approach is proposed which is about using feature extraction from images and deep learning algorithms to avoid the issue of the necessity of a large dataset. This work aims to improve the diagnostic of the Covid 19 virus in X-ray images, by extracting the features and applying the deep learning algorithm. This approach is composed of two main phases. The first one is based on feature extraction from images using feature extraction algorithms: Pyramid Histogram of Gradient (PHOG), Fourier, Gabor, and Discrete Cosine Transform (DCT). The second phase is based on using the last layers of CNN of deep learning for the classification problem. The experimentation of our approach is demonstrated by utilizing chest X-ray images obtained by PyImageSearch. Analysis of results shows that the proposed approach provides a satisfactory result. Our approach could be so beneficial in the future that it can be used to solve real-life problems even though insufficient data especially in urgent cases where there is not enough time to collect the data.

Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

A large number of countries depend on agriculture. However, the biggest challenge for farmers is the plant-related diseases. The most observed part of the plant is leaf but the segmentation of a diseased lesion leaf image suffers from uneven illumination and cluttered complex natural environment problems. To overcome these problems, the proposed methodology is based on the fusion of color balancing and superpixel. First, the input image transforms into a color-balanced image to eliminate the effects of uneven illumination. Second, compact regions are created from the transformed image using superpixel operation and the empirically derived threshold is applied on Histogram of Gradients (HOG) and color channels of superpixel to separate the unwanted background from leaf image. K-means clustering is applied to find a diseased infected image. An extended form of HOG, Pyramid of HOG(PHOG) with Gray Level Co-occurrence Matrix (GLCM) features are used to represent a diseased infected part. Finally, different classifiers are compared and Random Forest (RF) is selected to classify diseases. To assess the robustness of the proposed method, an experiment is conducted to test its accuracy effectively on natural images acquired from various farms, downloaded images from the Internet and Plant-Village color dataset. Results indicate that the proposed method achieved an accuracy of 93.12% on a combined dataset using cross-validation to show that method is effective to classify diseases in the presence of a cluttered background. The proposed system is compared with ALDD method (Dhaygude and Kumbhar, 2013), MVBPDR (Habib et al., 2018), AIMSD (Araujo and Peixoto, 2019) and CCF (Ma et al., 2017). The novelty of the proposed system lies in the fact that the proposed segmentation is effective for all the varied datasets and it is evaluated by True Positive Rate (TPR), False Positive Rate (FPR), False Negative Rate (FNR), Positive Prediction Value (PPV), False Discovery Rate (FDR), Accuracy and F1 score.

Fusing pyramid histogram of gradients and optical flow for hand gesture recognition

Human computer interaction systems based on hand gestures catch the eye of the research community for implementing natural communication between man and machines. However, different persons perform the same gestures differently in terms of velocity and motion scale. This poses a challenging issue in minimising the variations between different persons and maximises the coherence of the same gestures. In this paper, the original pyramid histogram of gradients in three orthogonal planes combining with the dense optical flow to create dynamic descriptor is explored in to discriminate features for recognition of hand gestures. The shape and motion features of images in a video sequence are captured to obtain the geometric and illumination invariant spatio-temporal feature descriptor for classification. A multiclass support vector machine classifier is used to recognise the hand gestures. The proposed method gives an excellent recognition rate and excels the existing approaches.

Learning combined features for automatic facial expression recognition

Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognising facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method. Before the feature extraction, we use Haar feature-based cascade classifier in order to detect then crop the face in the images. Next, we extract pyramid of histogram of gradients (PHOG) as shape descriptors and local binary patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called deep belief network (DBN). The experimental results on publicly available datasets show promising accuracy in recognising all expression classes, even for experiments which are evaluated on more than seven basic expressions.

Learning combined features for automatic facial expression recognition

Facial expressions are one of the most natural and powerful means for the human being in his social communications, whether to share his internal emotional states or to display his moods or intentions, which, in fact, may be true or simply played in a theatrical way. Given the numerous and variety of applications that can be easily planned, building a system able to automatically recognising facial expressions from images has been an intense field of study in recent years. In this paper, we propose a new framework for automatic facial expression recognition based on combined features and deep learning method. Before the feature extraction, we use Haar feature-based cascade classifier in order to detect then crop the face in the images. Next, we extract pyramid of histogram of gradients (PHOG) as shape descriptors and local binary patterns (LBP) as appearance features to form hybrid feature vectors. Finally, we use those vectors for training deep learning algorithm called deep belief network (DBN). The experimental results on publicly available datasets show promising accuracy in recognising all expression classes, even for experiments which are evaluated on more than seven basic expressions.

Enhanced Gabor (E-Gabor), Hypersphere-based normalization and Pearson General Kernel-based discriminant analysis for dimension reduction and classification of facial emotions

Abstract This paper puts forward an Enhanced Gabor feature descriptor termed as E-Gabor for obtaining high classification accuracy of emotions with low dimension. Two methods have been used for further classification. In the first method, E-Gabor is used as a stand-alone feature for classification. Hypersphere-based normalization has been used for normalizing the E-Gabor features, thereby improving the efficiency in the classification of emotions. In the second method, the E-Gabor feature descriptor is fused with Pyramid Histogram of Gradient (PHOG) feature descriptor and projected to a common subspace of six dimensions using the proposed Pearson General Kernel-based Discriminant Analysis (PGK-DA) before classification. In both the methods, Pearson General Kernel-based Extreme Learning Machine (PGK-ELM) is used for classification. Experiments conducted on Japanese Female Facial Expression (JAFFE), Cohn Kanade (CK+), Multimedia Understanding Group (MUG), Static Facial Expressions in the Wild (SFEW), Oulu-Chinese Academy of Science, Institute of Automation (Oulu-CASIA) and Man–Machine Interaction (MMI) datasets report a classification accuracy of 97.6%, 97.9%, 95.7%, 35.4%, 87.7% and 82.7% with method I and 95.7%, 97.2%, 94.9%, 35.2%, 87.1% and 82.1% with method II, respectively, for seven class emotion detection, which is high when compared to other state-of-the-art methods.