Optimization techniques for image representation in visual place recognition

In visual place recognition we aim to match a given query image from a query database with the most appropriate reference image from a reference database. One of the main issues is how to represent a place. Although an ordinary RGB representation can represent a place, various, either handcrafted or learned representations such as deep convolutional neural networks achieve better quantitative results. By using optimization techniques, both convex and non-convex, we can adapt a place representation such that it fits into the problem of visual place recognition. Therefore, in this paper we examine numerous optimization techniques and incorporate them in the context of our problem. Quantitatively, in terms of the area under a curve (AUC) measure, conducted experiments show how such optimized representation outperforms unoptimized one.