A variational statistical framework for object detection

Authors:
Wentao Fan;Nizar Bouguila;Djemel Ziou
Affiliations:
Concordia University, QC, Cannada;Concordia University, QC, Cannada;Sherbrooke University, QC, Cannada
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Year:
2011

Citing 18
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Abstract

In this paper, we propose a variational framework of finite Dirichlet mixture models and apply it to the challenging problem of object detection in static images. In our approach, the detection technique is based on the notion of visual keywords by learning models for object classes. Under the proposed variational framework, the parameters and the complexity of the Dirichlet mixture model can be estimated simultaneously, in a closed-form. The performance of the proposed method is tested on challenging real-world data sets.