Joint Optimization of Cascaded Classifiers for Computer Aided Detection

The existing methods for offline training of cascade classifiers take a greedy search to optimize individual classifiers in the cascade, leading inefficient overall performance. We propose a new design of the cascaded classifier where all classifiers are optimized for the final objective function. The key contribution of this paper is the AND-OR framework for learning the classifiers in the cascade. In earlier work each classifier is trained independently using the examples labeled as positive by the previous classifiers in the cascade, and optimized to have the best performance for that specific local stage. The proposed approach takes into account the fact that an example is classified as positive by the cascade if it is labeled as positive by all the stages and it is classified as negative if it is rejected at any stage in the cascade. An offline training scheme is introduced based on the joint optimization of the classifiers in the cascade to minimize an overall objective function...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Murat Dundar, Jinbo Bi
• Keywords: Cascade Classifiers, Computer Vision, CVPR 2007, Final Objective Function, Individual Classifiers, Inefficient Overall Performance, Overall Objective Function