Detector Ensemble

Component-based detection methods have demonstrated their promise by integrating a set of part-detectors to deal with large appearance variations of the target. However, an essential and critical issue, i.e., how to handle the imperfectness of part-detectors in the integration, is not well addressed in the literature. This paper proposes a detector ensemble model that consists of a set of substructuredetectors, each of which is composed of several partdetectors. Two important issues are studied both in theory and in practice, (1) finding an optimal detector ensemble, and (2) detecting targets based on an ensemble. Based on some theoretical analysis, a new model selection strategy is proposed to learn an optimal detector ensemble that has a minimum number of false positives and satisfies the design requirement on the capacity of tolerating missing parts. In addition, this paper also links ensemble-based detection to the inference in Markov random field, and shows that the target detect...

• Type: Conference
• Year: 2007
• Where: CVPR
• Authors: Shengyang Dai, Ming Yang, Ying Wu, Aggelos K. Katsaggelos
• Keywords: Component-based Detection Methods, Computer Vision, CVPR 2007, Detector Ensemble Model, Ensemble-based Detection, Optimal Detector Ensemble, Target Detection