Knowledge sharing • 2 min read •
Simple Solutions for Simple Problems: Classical Machine Learning in Computer Vision
Not every problem needs deep learning. In the age of AI, it's easy to overlook classical computer vision techniques, even though they remain highly effective for many practical tasks. Here's why these "old-school" methods still deserve a place in every machine learning engineer's toolbox.
The rapid rise of deep neural networks has led many to overlook the classical methods of image processing and computer vision. Yet these techniques remain highly effective for a wide range of practical applications.
Rather than museum pieces, classical approaches are best thought of as reliable hand tools: proven, efficient, and capable of solving many problems when applied appropriately.
Edge Detection
Edge detection identifies sharp changes in image intensity or brightness. It can be used, for example, to highlight the contours of boxes, making it easier to detect deformations or defects. One of the best-known algorithms for this task is the Canny edge detector.
Corner Detection and Feature Matching
Corner detection follows a similar principle but looks for intensity changes in multiple directions. Detecting these key points is a fundamental step in feature matching, where corresponding points are identified across different images. This technique is commonly used for applications such as object tracking in video.
Image Segmentation
Segmentation separates an image into meaningful regions. A common use case is isolating characters or numbers from an image before further processing.
Image Classification
Classical machine learning algorithms such as k-Nearest Neighbors (k-NN), Logistic Regression, and Support Vector Machines (SVMs) can perform well on smaller datasets. However, they generally fall short of the performance achieved by Convolutional Neural Networks (CNNs) on more complex image classification tasks.
Object Detection
Not every object detection problem requires a neural network. In some scenarios, a carefully designed pipeline combining Histogram of Oriented Gradients (HOG), SVM, Sliding Window, and Non-Maximum Suppression (NMS) can still deliver excellent results.
Why Does It Matter?
The veterans of computer vision should not be forgotten. In many cases, classical methods provide solutions that are simpler, more interpretable, faster, and less computationally demanding than deep learning models—making them an excellent choice for specific tasks or as components within larger computer vision pipelines.
