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.

Efficient facial expression recognition using histogram of oriented gradients in wavelet domain

Abstract: Facial expression recognition plays a significant role in human behavior detection. In this study, we present an efficient and fast facial expression recognition system. We introduce a new feature called W_HOG where W indicates discrete wavelet transform and HOG indicates histogram of oriented gradients feature. The proposed framework comprises of four stages: (i) Face processing, (ii) Domain transformation, (iii) Feature extraction and (iv) Expression recognition. Face processing is composed of face detection, cropping and normalization steps. In domain transformation, spatial domain features are transformed into the frequency domain by applying discrete wavelet transform (DWT). Feature extraction is performed by retrieving Histogram of Oriented Gradients (HOG) feature in DWT domain which is termed as W_HOG feature. For expression recognition, W_HOG feature is supplied to a well-designed tree based multiclass support vector machine (SVM) classifier with one-versus-all architecture. The proposed system is trained and tested with benchmark CK+, JAFFE and Yale facial expression datasets. Experimental results of the proposed method are effective towards facial expression recognition and outperforms existing methods.

Multi-view human action recognition using 2D motion templates based on MHIs and their HOG description

Abstract: In this study, a new multi-view human action recognition approach is proposed by exploiting low-dimensional motion information of actions. Before feature extraction, pre-processing steps are performed to remove noise from silhouettes, incurred due to imperfect, but realistic segmentation. Two-dimensional motion templates based on motion history image (MHI) are computed for each view/action video. Histograms of oriented gradients (HOGs) are used as an efficient description of the MHIs which are classified using nearest neighbor (NN) classifier. As compared with existing approaches, the proposed method has three advantages: (i) does not require a fixed number of cameras setup during training and testing stages hence missing camera-views can be tolerated, (ii) requires less memory and bandwidth requirements and hence (iii) is computationally efficient which makes it suitable for real-time action recognition. As far as the authors know, this is the first report of results on the MuHAVi-uncut dataset having a large number of action categories and a large set of camera-views with noisy silhouettes which can be used by future workers as a baseline to improve on. Experimentation results on multi-view with this dataset gives a high-accuracy rate of 95.4% using leave-one-sequence-out cross-validation technique and compares well to similar state-of-the-art approaches.

Bagged textural and color features for melanoma skin cancer detection in dermoscopic and standard images

Abstract: Early detection of malignant melanoma skin cancer is crucial for treating the disease and saving lives. Many computerized techniques have been reported in the literature to diagnose and classify the disease with satisfactory skin cancer detection performance. However, reducing the false detection rate is still challenging and preoccupying because false positives trigger the alarm and require intervention by an expert pathologist for further examination and screening. In this paper, an automatic skin cancer diagnosis system that combines different textural and color features is proposed. New textural and color features are used in a bag-of-features approach for efficient and accurate detection. We particularly claim that the Histogram of Gradients (HG) and the Histogram of Lines (HL) are more suitable for the analysis and classification of dermoscopic and standard skin images than the conventional Histogram of Oriented Gradient (HOG) and the Histogram of Oriented Lines (HOL), respectively. The HG and HL are bagged separately using a codebook for each and then combined with other bagged color vector angles and Zernike moments to exploit the color information. The overall system has been assessed through intensive experiments using different classifiers on a dermoscopic image dataset and another standard dataset. Experimental results have shown the superiority of the proposed system over state-of-the-art techniques.

Moving Object Localization Using Optical Flow for Pedestrian Detection from a Moving Vehicle

Abstract: This paper presents a pedestrian detection method from a moving vehicle using optical flows and histogram of oriented gradients (HOG). A moving object is extracted from the relative motion by segmenting the region representing the same optical flows after compensating the egomotion of the camera. To obtain the optical flow, two consecutive images are divided into grid cells 14 × 14 pixels; then each cell is tracked in the current frame to find corresponding cell in the next frame. Using at least three corresponding cells, affine transformation is performed according to each corresponding cell in the consecutive images, so that conformed optical flows are extracted. The regions of moving object are detected as transformed objects, which are different from the previously registered background. Morphological process is applied to get the candidate human regions. In order to recognize the object, the HOG features are extracted on the candidate region and classified using linear support vector machine (SVM). The HOG feature vectors are used as input of linear SVM to classify the given input into pedestrian/nonpedestrian. The proposed method was tested in a moving vehicle and also confirmed through experiments using pedestrian dataset. It shows a significant improvement compared with original HOG using ETHZ pedestrian dataset.