Optimally Combining a Cascade of Classifiers

Conventional approaches to combining classifiers improve accuracy at the cost of increased processing. We propose a novel search based approach to automatically combine multiple classifiers in a cascade to obtain the desired tradeoff between classification speed and classification accuracy. The search procedure only updates the rejection thresholds (one for each constituent classier) in the cascade, consequently no new classifiers are added and no training is necessary. A branch-and-bound version of depth-first-search with efficient pruning is proposed for finding the optimal thresholds for the cascade. It produces optimal solutions under arbitrary user specified speed and accuracy constraints. The effectiveness of the approach is demonstrated on handwritten character recognition by finding a) the fastest possible combination given an upper bound on classification error, and also b) the most accurate combination given a lower bound on speed.