Image Feature Extraction Using Gradient Histograms to Generate Feature Vectors

In this document, we explore image feature extraction and demonstrate how to implement gradient histogram-based feature vector extraction using MATLAB code. Image feature extraction is a computer vision technique that converts visual information into numerical representations, enabling subsequent analysis and processing. The gradient histogram method is a widely-used feature extraction approach that captures edge and texture information by calculating gradient magnitudes and orientations for each pixel in an image.

The MATLAB implementation typically involves several key steps: First, preprocessing the input image (converting to grayscale if necessary) using rgb2gray(). Then computing horizontal and vertical gradients through convolution with Sobel operators via imgradientxy(). The gradient magnitudes and directions are calculated using imgradient(), followed by binning the orientations into a histogram using histcounts(). The resulting normalized histogram forms the feature vector that encodes the image's structural characteristics.

This technique produces compact feature vectors suitable for various computer vision applications including image classification (using classifiers like SVM), object detection, and image recognition systems. The gradient histogram method is particularly effective for capturing local shape information while maintaining illumination invariance.