Today, image classification is one of the most important fields of computer vision, attracting the attention of many academics due to its wide range of practical applications, which include human identification, facial recognition, object categorization, and medical disease diagnosis. A wide range of local and global image descriptors have been developed to solve image classification problems.
Finding a strong descriptor that can be used to differentiate across classes is an important initial step. Color Local Binary Pattern (CLBP), Scale Invariant Feature Transform (SIFT), Histogram of Oriented Gradients (HOGs), and GIST are just a few of the best image descriptors available.
HOG, for example, is a productive descriptor with a variety of practical applications, including pedestrian detection, face recognition, item classification, security, and industrial inspection.
In this post, we will take a thorough look at the Histogram of Oriented Gradients (HOG), its operation, significance, and applications, as well as the problems. Let’s get started.
What is HOG – Histogram of Oriented Gradients?
In computer vision and image processing, the Histogram of Oriented Gradients (HOG) approach is used to explain the characteristics of a particular piece of data. It takes into account how frequently a particular area of a picture displays a gradient orientation.
HOG, also known as Histogram of Oriented Gradients, is a feature descriptor similar to the Canny Edge Detector, SIFT (Scale Invariant and Feature Transform). It serves the aim of object detection in computer vision and image processing. In the confined area of a picture, the method counts instances of gradient orientation.
This technique is extremely similar to Scale Invariant and Feature Transformation and Edge Orientation Histograms (SIFT). The HOG description emphasizes an object’s structure or
Since it computes the features using both the magnitude and the angle of the gradient, it is superior to other edge descriptors. It creates histograms for the areas of the picture
based on the gradient’s magnitude and directions.
Now, you must be wondering what the heck is a feature descriptor. Not to worry, I got you.
A feature descriptor is an image or image patch representation that simplifies the picture by extracting important information and discarding superfluous information.
A feature descriptor typically translates an image of width x height x 3 (channels) to a feature vector/array of length n. The input picture for the HOG feature descriptor is 64 x 128 x 3, while the output feature vector is 3780 in length.
Keep in mind that the HOG descriptor can be computed for various sizes. However, I am keeping to the figures supplied in the original research so that you can grasp the concept with a single concrete example.
The distribution of gradient axes (represented by histograms of oriented gradients) is utilized as a feature in the HOG feature descriptor. The x and y derivatives of an image’s gradients (regions of abrupt intensity changes) are significant around edges and corners, and we know that edges and corners contain a lot of info about the shape of the object other than flat regions. For this reason, gradients (regions of abrupt intensity changes) are useful.
Working of HOG
HOG is a technique for extracting dense picture features. In contrast to SIFT, which only extracts features for the immediate vicinity of key points, dense denotes that it extracts features for all places in the picture (or a region of interest in the image).
It naturally seeks to record the geometry of the buildings in the area by recording gradient information. To achieve this, it divides the image into tiny blocks of 4×4 cells and cells that are typically 8×8 pixels in size. The number of gradient orientation bins in each cell is fixed.
With a vote proportionate to the gradient magnitude at that pixel, each pixel in the cell casts a ballot for a certain gradient orientation bin.
To minimize aliasing, the pixel votes are bilinearly interpolated. Both orientation and location are interpolated. This is essential because it indicates that a pixel will cast a vote for the orientation bins close to it in addition to its orientation bin (For e.g., if the gradient orientation at a pixel is 45 degrees, it will vote with a weight of 0.5 for the 35 to 45-degree bin and a weight of 0.5 for the 45 to 55-degree bin).
Similarly, it will vote for these two orientation bins in all four cells around it in addition to its cell. The distance between the pixel centers and the cell centers determines the weights.
Additionally, histograms are regularized using their energy (L2 norm across blocks). A cell will be a part of 4 blocks because the blocks have a 1-cell step size.
The histogram of the cell is defined in terms of four distinct normalized versions. These four histograms are catenated to obtain the cell’s descriptor. The components of histograms are frequently limited to a certain value as well.
Significance of HOG
HOG performs significantly better than wavelets. Also, any appreciable amount of smoothing before calculating gradients degrades the HOG results. These things highlight the fact that a large portion of the image information that is currently available comes from abrupt edges at fine scales, and blurring this in the hopes of lessening its sensitivity to the spatial position is a mistake.
In the present pyramid layer, gradients should instead be computed at the smallest scale possible, corrected, or utilized for orientation voting, and only then should they be spatially blurred. In light of this, rather coarse spatial quantization (88-pixel cells/one limb width) is enough.
Second, considerable local contrast normalization is required for successful results, making conventional center-surround style schemes an inferior option. Better results can be obtained by normalizing each element (edge, cell) concerning various local supports and considering the results as separate signals.
The inclusion of this “redundant” information boosts performance from 84% to 89% at 10-4 FPPW in the conventional detector, where each HOG cell appears four times with various normalizations.
Applications of HOG
A reliable descriptor known as the histogram of oriented gradient (HOG) is often utilized in many practical applications, such as human detection, face recognition, item counting, and video surveillance.
Future developments in image identification and face detection will surely be greatly aided by the Histogram of Oriented Gradients object detection approach. This subject can have a significant influence on several sectors, from enhancing AR tools to enhancing blind people’s eyesight.
I’m very enthusiastic about the application in the area of autonomous cars. It seems reasonable that not many businesses are adopting HOG in their self-driving cars, given the wide range of techniques to computer vision that may be used to detect objects and vehicles. I hope several businesses see the potential of HOG and use it in the future.
Issues of HOG
It does have the drawback of being extremely susceptible to picture rotation. HOG is, therefore, a poor choice for classifying textures or things that are frequently identified as rotating images.
In conclusion, feature descriptors, which isolate the relevant data and exclude the irrelevant data, serve as the foundation for HOG. HOG calculates the amount and direction of the gradient’s horizontal and vertical components for each pixel, then arranges the data into a 9-bin histogram to identify changes in the data.
The Histogram of Oriented Gradients (HOG) approach is employed for object detection and picture recognition. It may be applied anywhere, including in the fields of driverless cars and augmented reality (mostly anything involving image detection).