This paper presents a cognition-inspired agnostic framework for building a map for Visual Place Recognition. This framework draws inspiration from human-memorability, utilizes the traditional image entropy concept and computes the static content in an image; thereby presenting a tri-folded criterion to assess the 'memorability' of an image for visual place recognition. A dataset namely 'ESSEX3IN1' is created, composed of highly confusing images from indoor, outdoor and natural scenes for analysis. When used in conjunction with state-of-the-art visual place recognition methods, the proposed framework provides significant performance boost to these techniques, as evidenced by results on ESSEX3IN1 and other public datasets.
@article{arxiv.1811.03529,
title = {Memorable Maps: A Framework for Re-defining Places in Visual Place Recognition},
author = {Mubariz Zaffar and Shoaib Ehsan and Michael Milford and Klaus Mcdonald Maier},
journal= {arXiv preprint arXiv:1811.03529},
year = {2019}
}