neural networks; evolutionary algorithm; keypoint; optimization

This contribution presents the use of neural networks trained by an evolutionary algorithm for a selection of visual keypoints. Visual keypoints play an important role in many computer vision tasks but many algorithms for keypoint detection produce many keypoints which are not useful for the target task. We aim to filter them in a data-driven way. Our model uses a neural network that ranks each keypoint by a relevancy score that we use to choose top-K keypoints with the highest rank. These keypoints are then used for the target task, which is image classification in our case. Because we use discrete operations in our model, we can not easily obtain gradients for weight updates. We, therefore, optimize the weights of the network by CMA-ES algorithm, which enables efficient optimization of continuous parameters of black-box functions. In this article, we present our initial experiments with this method.