python hog svm

Python HOG SVM refers to the use of the Histogram of Oriented Gradients (HOG) feature descriptor and Support Vector Machine (SVM) classifier in Python for object detection and recognition tasks.

HOG is a popular feature descriptor used for object detection in computer vision. It works by breaking down an image into small cells and computing the gradient orientation of each pixel within each cell. These gradient orientations are then used to create a histogram of gradients, which serves as a feature descriptor for the image.

SVM is a machine learning algorithm that can be used for classification tasks. It works by finding the optimal hyperplane that separates the data into different classes. In object detection and recognition tasks, SVM is commonly used as a classifier to distinguish between objects and background.

In Python, the HOG feature descriptor can be implemented using the scikit-image library, while the SVM classifier can be implemented using the scikit-learn library. By combining these two techniques, it is possible to build an effective system for object detection and recognition in Python.