Incremental threshold learning for classifier selection

Abstract

Threshold-based classifier is a simple yet powerful pattern classification tool, which has been frequently used in applications of object detection and recognition. A threshold-based classifier is usually associated with a unique one-dimensional feature. A properly selected threshold and a binary sign corresponding to the feature govern the classifier. However, the learning process is usually done in a batch manner. The batch algorithms are not suitable for sequentially incoming data because of the limitation of storage and prohibitive computation cost. To deal with sequentially incoming data, this paper proposes an incremental algorithm for incrementally learning the threshold-based classifiers. The proposed method can not only incrementally model the features but also estimate the threshold and training error in a close form. The effectiveness of the proposed algorithm is evaluated in the applications of gender recognition, face detection, and human detection.