Histogram of oriented gradients

About: Histogram of oriented gradients is a research topic. Over the lifetime, 2037 publications have been published within this topic receiving 55881 citations. The topic is also known as: HOG.

Genetic prOgramming for image feature descriptor learning

Abstract: It is widely accepted that feature extraction is quite possibly the most critical step in computer vision. Typically, feature extraction is performed using a method such as the histogram of oriented gradients. In recent years, a shift has occurred from human to machine learned features, e.g., convolutional neural networks (CNNs) and Evolution-COnstructed (ECO) features. An advantage of our improved ECO (iECO) framework is it optimizes features on a per-descriptor basis. Herein, iECO is extended in order to represent a richer class of features, namely arithmetic combinations and compositions of iECOs. This extension, called Genetic prOgramming Optimal Feature Descriptor (GOOFeD) is based on genetic programming (GP). Three experiments are performed on data from a U.S. Army test site that contains multiple target and clutter types, burial depths, and times of day for automatic buried explosive hazard detection. The first two experiments focus on GOOFeD initialization and parameter selection. The last experiment demonstrates that GOOFeD is superior to iECO in terms of the fitness of evolved individuals.

Histogram of Oriented Gradients Feature Extraction Without Normalization

Abstract: In this paper, the effects of normalization in the histogram of oriented gradients (HOG) are studied and a HOG feature extraction pipeline without normalization is proposed. In the proposed pipeline, the functionality of normalization is merged into the gradient generation step by replacing the original linear difference based gradients with logarithmic gradients. Due to the discrete property of the pixel values, the logarithmic operation can be easily implemented using a lookup table (LUT) with a depth of 2N, where N is the bit-width of the pixels. Theoretical analysis and experimental results show that the proposed normalization-free HOG feature based logarithmic gradient is close to the original version and can be used in the pedestrian detection algorithms without performance degradation. It is shown in the experiments that by skipping the time-consuming normalization step, the processing speed of HOG feature extraction can be significantly improved.

An Effective Visual Descriptor Based on Color and Shape Features for Image Retrieval

Abstract: In this paper we present a Content-Based Image Retrieval (CBIR) system which extracts color features using Dominant Color Correlogram Descriptor (DCCD) and shape features using Pyramid Histogram of Oriented Gradients (PHOG). The DCCD is a descriptor which extracts global and local color features, whereas the PHOG descriptor extracts spatial information of shape in the image. In order to evaluate the image retrieval effectiveness of the proposed scheme, we used some metrics commonly used in the image retrieval task such as, the Average Retrieval Precision (ARP), the Average Retrieval Rate (ARR) and the Average Normalized Modified Retrieval Rank (ANMRR) and the Average Recall (R)-Average Precision (P) curve. The performance of the proposed algorithm is compared with some other methods which combine more than one visual feature (color, texture, shape). The results show a better performance of the proposed method compared with other methods previously reported in the literature.

Occlusion handling and human detection based on Histogram of Oriented Gradients for automatic video surveillance

Abstract: Human detection in a video surveillance system has vast application areas including suspicious event detection and human activity recognition. In the current environment of our society suspicious event detection is a burning issue. For that reason, this paper proposes a framework for detecting humans in different appearances and poses by generating a human feature vector. Initially, every pixel of a frame is represented as an incorporation of several Gaussians and use a probabilistic method to refurbish the representation. These Gaussian representations are then estimated to classify the background pixels from foreground pixels. Shadow regions are eliminated from foreground by utilizing a Hue-Intensity disparity value between background and current frame. Then morphological operation is used to remove discontinuities in the foreground extracted from the shadow elimination process. Partial occlusion handling is utilized by color correlogram to label objects within a group. After that, the framework generates ROIs by determining which of the foregrounds represent human by considering conditions related to human body. Finally, Histogram of Oriented Gradients (HOG) feature is extracted from ROI for classification. Various videos containing moving humans are utilized to evaluate the proposed framework and presented outcomes demonstrate the adequacy.

Automated detection of age-related macular degeneration using a pre-trained deep-learning scheme

Abstract: An eye disease affects the entire sensory operation, and an unrecognised and untreated eye disease may lead to loss of vision. The proposed work aims to develop an automated age-related macular degeneration (AMD) detection system using a Deep-Learning (DL) scheme with serially concatenated deep and handcrafted features. The research includes the following phases: initial data processing, deep-features extraction with VGG16, handcrafted feature extraction, optimal feature selection using Mayfly-Algorithm, serial features concatenation, and binary classification and validation. In this work, the handcrafted features, such as local binary pattern (LBP), pyramid histogram of oriented gradients (PHOG), and discrete wavelet transform (DWT), are extracted from the test images and concatenated with the deep-features of VGG16. The performance of the developed system is separately tested using fundus retinal images (FRI) and optical coherence tomography (OCT) images. A binary classification with a fivefold cross validation is employed and the best outcome of this trial is chosen as the result. The performance of VGG16 is then compared with that of VGG19, ResNet50, and AlexNet. The experimental outcome of this research confirms that the proposal of VGG16 with concatenated features achieves AMD detection accuracies of 97.08 and 97.50 for FRI and OCT images, respectively.