What is Histogram of Oriented Gradients (HOG)?

Histogram of Oriented Gradients (HOG) is a feature extraction technique widely used in computer vision and image processing for object detection and image recognition. It was initially introduced by Navneet Dalal and Bill Triggs in 2005. HOG is particularly useful for detecting objects within images based on their shape and texture.

Here’s an overview of how HOG works:

Image Gradient Calculation:

HOG starts by computing the gradient of the image. This is usually done using methods like the Sobel or Scharr operators. The gradient represents the change in intensity or color at each pixel, indicating the edges and texture patterns in the image.

Gradient Orientation Binning:

The image is divided into small cells, typically with dimensions like 8×8 or 16×16 pixels.

Within each cell, the gradient orientations are binned into a histogram. These histograms capture the distribution of gradient orientations within each cell.

The gradient orientations are divided into a predefined number of orientation bins (e.g., 9 bins covering 0 to 180 degrees). Each pixel contributes to one or more bins based on its gradient orientation.

Block Normalization:

To enhance the robustness of the feature representation and reduce the effects of lighting variations, contrast changes, and shadowing, HOG employs block normalization.

The image is further divided into blocks that overlap with each other. A common choice is a 2×2 block.

Within each block, the histograms from the cells are concatenated.

These concatenated histograms are then normalized using various methods, such as L2 normalization or signed square root.

Descriptor Generation:

The normalized histograms from all the blocks in the image are concatenated to create a final feature vector, which is often referred to as the HOG descriptor.

This descriptor represents the distribution of gradient orientations and their magnitudes over the entire image.

Training and Classification:

HOG descriptors can be used as input features for machine learning algorithms, such as support vector machines (SVMs) or neural networks.

In object detection or image recognition tasks, a classifier is trained on a dataset of HOG descriptors from positive and negative examples to distinguish between objects of interest and background.

Sliding Window Detection:

In object detection applications, a sliding window approach is often used, where the HOG descriptor is computed for each window of the image, and the classifier is applied to determine whether an object of interest is present in each window.

HOG has been particularly successful in pedestrian detection and face detection tasks. However, it can also be applied to a wide range of object recognition tasks where shape and texture information are crucial for accurate detection.

While HOG is a powerful feature extraction method, it has been largely superseded in recent years by deep learning techniques, such as Convolutional Neural Networks (CNNs), which can learn hierarchical features directly from the raw pixel data.

To explain better, here’s a step-by-step explanation of the Histogram of Oriented Gradients (HOG) feature extraction technique:

Input:

Input image (grayscale or color).

Parameters:

• Cell size: The size of each cell for gradient computation (e.g., 8×8 pixels).
• Block size: The size of each block that encompasses multiple cells (e.g., 2×2 cells per block).
• Number of orientation bins: The number of histogram bins to quantize gradient orientations (e.g., 9 bins).
• Descriptor normalization method: The method used to normalize histograms within each block (e.g., L2 normalization).

Output:

HOG descriptor for the input image.

Steps:

1. Gradient Calculation:

• Convert the input image to grayscale if it’s a color image.
• Calculate the gradient magnitude and orientation for each pixel in the image using methods like the Sobel or Scharr operators.

2. Histogram Computation in Cells:

Divide the image into non-overlapping cells of the specified size (e.g., 8×8 pixels).

For each cell:

• Initialize an array to store gradient orientation histograms with the specified number of bins (e.g., 9 bins).
• Loop through the pixels in the cell:
  • For each pixel, calculate its gradient magnitude and orientation.
  • Distribute the gradient magnitude into the appropriate orientation bins based on the gradient orientation.
• The result is a histogram of gradient orientations for the current cell.

3. Block Normalization:

Divide the image into blocks, which can overlap with each other (e.g., 2×2 cells per block).

For each block:

• Concatenate the histograms of all the cells within the block into a single vector.
• Apply a normalization method (e.g., L2 normalization) to the concatenated vector.
• The normalized vector represents the HOG descriptor for the current block.

4. Descriptor Generation:

Concatenate the normalized block descriptors from all the blocks in the image.

This forms the final HOG descriptor for the entire image.

5. Optional: Classifier Training and Classification:

Train a machine learning classifier (e.g., SVM) using a dataset of HOG descriptors from positive and negative examples for the object you want to detect or recognize.

In classification tasks, you can apply the trained classifier to classify objects based on their HOG descriptors.

6. Object Detection (if applicable):

• In object detection tasks, slide a detection window over the input image.
• For each window, compute the HOG descriptor.
• Use the trained classifier to determine whether an object of interest is present within the window.

The resulting HOG descriptor encodes information about the distribution of gradient orientations and their magnitudes in the image, making it suitable for various computer vision tasks like object detection and recognition, especially when traditional computer vision methods are applied.