Authors:
Francisco Rodolfo Barbosa-Anda
1
;
Cyril Briand
1
;
Frédéric Lerasle
1
and
Alhayat Ali Mekonnen
2
Affiliations:
1
CNRS, LAAS, Univ de Toulouse, UPS and LAAS, France
;
2
CNRS and LAAS, France
Keyword(s):
Mathematical Programming, Soft-Cascade, Machine Learning, Object Detection.
Related
Ontology
Subjects/Areas/Topics:
Artificial Intelligence
;
Business Analytics
;
Cardiovascular Technologies
;
Computing and Telecommunications in Cardiology
;
Data Engineering
;
Decision Support Systems
;
Decision Support Systems, Remote Data Analysis
;
Health Engineering and Technology Applications
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Linear Programming
;
Methodologies and Technologies
;
Operational Research
;
Optimization
;
Symbolic Systems
Abstract:
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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