Improving robustness of multiple-objective genetic programming for object detection

Authors:
Rachel Hunt;Mark Johnston;Mengjie Zhang
Affiliations:
School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand;School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, Wellington, New Zealand;School of Engineering and Computer Science, Victoria University of Wellington, Wellington, New Zealand
Venue:
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Year:
2011

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Abstract

Object detection in images is inherently imbalanced and prone to overfitting on the training set. This work investigates the use of a validation set and sampling methods in Multi-Objective Genetic Programming (MOGP) to improve the effectiveness and robustness of object detection in images. Results show that sampling methods decrease runtimes substantially and increase robustness of detectors at higher detection rates, and that a combination of validation together with sampling improves upon a validation-only approach in effectiveness and efficiency.