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.

Building block level urban land-use information retrieval based on Google Street View images

Abstract: Land-use maps are important references for urban planning and urban studies. Given the heterogeneity of urban land-use types, it is difficult to differentiate different land-use types based on overhead remotely sensed data. Google Street View (GSV) images, which capture the facades of building blocks along streets, could be better used to judge the land-use types of different building blocks based on their facade appearances. Recently developed scene classification algorithms in computer vision community make it possible to categorize different photos semantically based on various image feature descriptors and machine-learning algorithms. Therefore, in this study, we proposed a method to derive detailed land-use information at building block level based on scene classification algorithms and GSV images. Three image feature descriptors (i.e., scale-invariant feature transform-Fisher, histogram of oriented gradients, GIST) were used to represent GSV images of different buildings. Existing land-use maps were...

Multistage Particle Windows for Fast and Accurate Object Detection

Abstract: The common paradigm employed for object detection is the sliding window (SW) search. This approach generates grid-distributed patches, at all possible positions and sizes, which are evaluated by a binary classifier: The tradeoff between computational burden and detection accuracy is the real critical point of sliding windows; several methods have been proposed to speed up the search such as adding complementary features. We propose a paradigm that differs from any previous approach since it casts object detection into a statistical-based search using a Monte Carlo sampling for estimating the likelihood density function with Gaussian kernels. The estimation relies on a multistage strategy where the proposal distribution is progressively refined by taking into account the feedback of the classifiers. The method can be easily plugged into a Bayesian-recursive framework to exploit the temporal coherency of the target objects in videos. Several tests on pedestrian and face detection, both on images and videos, with different types of classifiers (cascade of boosted classifiers, soft cascades, and SVM) and features (covariance matrices, Haar-like features, integral channel features, and histogram of oriented gradients) demonstrate that the proposed method provides higher detection rates and accuracy as well as a lower computational burden w.r.t. sliding window detection.

Inter-modality Face Sketch Recognition

Abstract: Automatic face sketch recognition plays an important role in law enforcement. Recently, various methods have been proposed to address the problem of face sketch recognition by matching face photos and sketches, which are of different modalities. However, their performance is strongly affected by the modality difference between sketches and photos. In this paper, we propose a new face descriptor based on gradient orientations to reduce the modality difference in feature extraction stage, called Histogram of Averaged Oriented Gradients (HAOG). Experiments on CUFS database show that the new descriptor outperforms the state-of-the-art approaches.

Improving handwriting based gender classification using ensemble classifiers

Abstract: A system to predict gender from images of handwriting using textural descriptors.Multiple classifiers to discriminate male and female writings.Classifiers combined using bagging, voting and stacking techniques.Generic and script-independent approach applied to English and Arabic handwritings.Improved results on the QUWI database once compared to state-of-the-art methods. This paper presents a system to predict gender of individuals from offline handwriting samples. The technique relies on extracting a set of textural features from handwriting samples of male and female writers and training multiple classifiers to learn to discriminate between the two gender classes. The features include local binary patterns (LBP), histogram of oriented gradients (HOG), statistics computed from gray-level co-occurrence matrices (GLCM) and features extracted through segmentation-based fractal texture analysis (SFTA). For classification, we employ artificial neural networks (ANN), support vector machine (SVM), nearest neighbor classifier (NN), decision trees (DT) and random forests (RF). Classifiers are then combined using bagging, voting and stacking techniques to enhance the overall system performance. The realized classification rates are significantly better than those of the state-of-the-art systems on this problem validating the ideas put forward in this study.

HOG-based approach for leaf classification

Abstract: In this paper, we propose a new approach for plant leaf classification, which treat histogram of oriented gradients (HOG) as a new representation of shape, and use the Maximum Margin Criterion (MMC) for dimensionality reduction. We compare this algorithm with a classic shape classification method Inner-Distance Shape Context (IDSC) on Swedish leaf dataset and ICL dataset. The proposed method achieves better performance compared with IDSC.