Improving the performance of an ensemble-based exudate detection system using stochastic parameter optimization

In this paper, we propose an efficient way to determine the optimal parameter setting of an ensemble based system dedicated to the detection of exudates in retinal images. We show that the optimal parameter settings of an individual detector may be different, when it becomes a member of an ensemble. We consider a stochastic search algorithm to solve this optimization problem. However, since the computational demand is extremely high, we introduce a specific speed-up by sampling the test dataset in every search step. We show that this approach is equivalent to the noisy evaluation of the energy function and fits the corresponding theoretical results to our case. Experimental results showing improvement with respect to the member exudate detectors and individually optimal parameter settings are presented for publicly available datasets.