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Communication Dans Un Congrès Année : 2016

Mean Response-Time Minimization of a Soft-Cascade Detector

Résumé

In this paper, the problem of minimizing the mean response-time of a soft-cascade detector is addressed. A soft-cascade detector is a machine learning tool used in applications that need to recognize the presence of certain types of object instances in images. Classical soft-cascade learning methods select the weak classifiers that compose the cascade, as well as the classification thresholds applied at each cascade level, so that a desired detection performance is reached. They usually do not take into account its mean response-time, which is also of importance in time-constrained applications. To overcome that, we consider the threshold selection problem aiming to minimize the computation time needed to detect a target object in an image (i.e., by classifying a set of samples). We prove the NP-hardness of the problem and propose a mathematical model that takes benefit from several dominance properties, which are put into evidence. On the basis of computational experiments, we show that we can provide a faster cascade detector, while maintaining the same detection performances.
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Dates et versions

hal-01282068 , version 1 (03-03-2016)

Identifiants

  • HAL Id : hal-01282068 , version 1

Citer

Francisco Rodolfo Barbosa-Anda, Cyril Briand, Frédéric Lerasle, Alhayat Ali Mekonnen. Mean Response-Time Minimization of a Soft-Cascade Detector. International Conference on Operations Research and Enterprise Systems, INSTICC, Feb 2016, Roma, Italy. pp.252-260. ⟨hal-01282068⟩
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