  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.
