Abstract 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.

中文翻译：

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使用模拟退火进行集成优化的基于有效采样的能量函数评估

摘要 在这项研究中，我们尝试开发一种方法，通过使用模拟退火来加速大型图像数据集上的目标检测器集合的参数优化。我们提出了一种新颖的基于采样的评估方法，该方法考虑了每次迭代所需的数据集的最小部分，以保持解决方案的质量。这种方法可以被认为是对能量的一种嘈杂的评估。搜索过程中所需的样本大小理论上是通过调整收敛结果进行噪声评估来确定的。为了确定适用性，我们准备并优化了两个集合，用于基于微动脉瘤检测的糖尿病视网膜病变预筛选，以及基于卷积神经网络和传统目标检测器的微动脉瘤检测。