Color, Shape and Texture: Feature Extraction using OpenCV
Do I start going through each column of the image and get each single pixel out?
I have wondered, for the longest time since I started learning computer vision, how does one extract features from an image? I first heard of the term “feature extraction” in a machine learning video tutorial on YouTube, which clearly explained how we could extract features in a large dataset. Very simply, the columns of the dataset are the features. However, when I came across computer vision topics, I was taken aback when I heard we would be extracting features from an image. Do I start going through each column of the image and get each single pixel out? That was exactly what went through in my mind!
Fast forward some time later, I now understand what feature extraction means in computer vision. Feature extraction is a part of the dimensionality reduction process, in which, an initial set of the raw data is divided and reduced to more manageable groups. In simpler terms, for an image, each pixel is a piece of data and what image processing does is extract only useful information from the image, hence reducing the amount of data but retaining the pixels that describe the image characteristics.
What image processing does is extract only useful…
