CascadeKlassifikatoren

CascadeKlassifikatoren, often referred to as Cascade Classifiers, are a machine learning approach primarily used for object detection. They were popularized by Paul Viola and Michael Jones in their 2001 paper on rapid object detection. The core idea is to use a series of progressively more complex classifiers to identify the presence of an object.

The cascade starts with a very simple and fast classifier that can quickly reject a large number of negative (non-object) samples. If a region of an image passes this initial classifier, it is then fed to the next, slightly more complex classifier. This process continues through several stages. Each stage is trained to have a very high detection rate (recall) and a low false positive rate. This hierarchical structure allows for efficient processing, as computationally expensive classifiers are only applied to a small subset of the image that has already passed earlier, simpler checks.

The features commonly used in Cascade Classifiers are Haar-like features, which are simple rectangular features that measure differences in pixel intensities in adjacent regions. These features are computationally efficient to calculate. Training a cascade classifier involves training each stage individually. The earlier stages are trained on a large dataset of positive (object) and negative (non-object) examples. Subsequent stages are trained on the harder negative examples that were misclassified by the previous stages.

Cascade Classifiers have been widely used for tasks like face detection, where they achieved significant real-time performance for their time. While newer deep learning methods have since surpassed their accuracy in many object detection benchmarks, the Cascade Classifier approach remains an important concept in the history of computer vision and object detection.