Deep learning features exception for cross-season visual place recognition

The use of Convolutional Neural Networks (CNNs) in image analysis and recognition paved the way for long-term visual place recognition. The transferable power of generic descriptors extracted at different layers of off-the-shelf CNNs has been successfully exploited in various visual place recognition scenarios. In this paper we tackle this problem by extracting the full output of an intermediate layer and building an image descriptor of lower dimensionality by omitting the activation of filters corresponding to environmental changes. Thus, we are able to increase the robustness of the cross-season visual place recognition. We test our approach on the Nordland dataset, the biggest and the most challenging dataset up to date, where the included four seasons induce great illumination and appearance changes. The experiments show that our new approach can significantly increase, up to 14%, the baseline (single-image search) performance of deep features.