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Efficient sampling-based energy function evaluation for ensemble optimization using simulated annealing
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DOI：10.1016/j.patcog.2020.107510.png)
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Abstract
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In this study, we attempted to develop a method for accelerating parameter optimization of an object detector ensemble over large image datasets by using simulated annealing. We propose a novel sampling-based evaluation method that considers the minimum portion of the dataset required in each iteration to maintain solution quality. This approach can be considered a noisy evaluation of the energy. The sample sizes required during the search process are theoretically determined by adapting the convergence results for noisy evaluation. To determine applicability, we prepared and optimized two ensembles for diabetic retinopathy pre-screening based on microaneurysm detection with convolutional neural network-based and traditional object detectors. Our experimental results indicate that the proposed sampling-based evaluation method substantially reduced the computational time required for optimizing the parameters of the ensembles while preserving solution quality. (C) 2020 The Authors. Published by Elsevier Ltd.
Keywords:
Diabetic retinopathy
Ensemble
Microaneurysm detection
Parameter optimization
Sampling-based evaluation
Simulated annealing
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