The feature vector produced by these algorithms when fed into an image classification algorithms like Support Vector Machine (SVM) produce good results.But, what kinds of “features” are useful for classification tasks ? The key takeaway here is that if you can consistently detect and extract the ROI of your image dataset, the HOG descriptor should definitely be on your list of image descriptors to apply, as it’s very powerful and able to obtain good results, especially when applied in conjunction with machine learning.HOG descriptors are very powerful; however, it can be tedious to choose the correct parameters for the number of
To incerease the size of negative set we extract patches of image at different scale using PatchExtractor from Scikit-Learn.Scikit-Image's feature module offers a function skimage.feature.hog which extracts Histogram of Oriented Gradients (HOG) features for a given image. Notice how the direction of arrows points to the direction of change in intensity and the magnitude shows how big the difference is.On the right, we see the raw numbers representing the gradients in the 8×8 cells with one minor difference — the angles are between 0 and 180 degrees instead of 0 to 360 degrees. One of the following norms could be used:Now, we could simply normalize the 9×1 histogram vector but it is better to normalize a bigger sized block of 16×16.
The only constraint is that the patches being analyzed have a fixed aspect ratio. All views expressed on this site are my own and do not represent the opinions of OpenCV.org or any entity whatsoever with which I have been, am now, or will be affiliated.
normalisations. Why not 32×32 ? Since 10 degrees is half way between 0 and 20, the vote by the pixel splits evenly into the two bins.There is one more detail to be aware of. It is used to improve performance of the HOG descriptor.
Gradients ( x and y derivatives ) of an image are useful because the magnitude of gradients is large around edges and corners ( regions of abrupt intensity changes ) and we know that edges and corners pack in a lot more information about object shape than flat regions.In this section, we will go into the details of calculating the HOG feature descriptor.
constant colored background ), but highlighted outlines. keypoints,” International Journal of Computer Vision, 60, 2 (2004),
to next stage. It is shown that by taking advantage of the inter-channel correlation of natural images, the HOG features can be directly extracted from the Bayer pattern … Clearly, the feature vector is not useful for the purpose of viewing the image. But for now just understand that HOG is mainly used as a descriptor for object detection and that later these descriptors can be fed into a machine learning classifier.HOG is implemented in both OpenCV and scikit-image. Let’s first focus on the pixel encircled in blue.
color channel is used, which provides color invariance to a large However, the OpenCV implementation is not very flexible and is primarily geared towards the Dalal and Triggs implementation. If you continue to use this site we will assume that you are happy with it. In other words, you can look at the gradient image and still easily say there is a person in the picture.At every pixel, the gradient has a magnitude and a direction. In most real-world applications, HOG is used in conjunction with a Linear SVM to perform object detection.
cells and contrast normalises their overall responses before passing A 16×16 block has 4 histograms (8×8 cell results to one histogram) which can be concatenated to form a 36 x 1 element vector and normalized. The OpenCV implementation is less flexible than the scikit-image implementation, and thus we will primarily used the scikit-image implementation throughout the rest of this course.In this lesson, we will be discussing the Histogram of Oriented Gradients image descriptor in detail.HOG descriptors are mainly used to describe the structural shape and appearance of an object in an image, making them excellent descriptors for object classification. After reviewing existing edge and gradient based descriptors, we show experimentally that grids of histograms of oriented gradient (HOG) descriptors significantly outperform existing feature sets for human detection. Human Detection,” IEEE Computer Society Conference on Computer The adopted method pools gradient orientation See image on the side. Dense means that it extracts features for all locations in the image (or a region of interest in the image) as opposed to only the local neighborhood of keypoints like SIFT. Dalal and Triggs report that using either Here is an example where we have taken an input region of an image, computed a gradient histogram for each cell, and then locally grouped the For each of the cells in the current block we concatenate their corresponding gradient histograms, followed by either L1 or L2 normalizing the entire concatenated feature vector.