Title: User-driven mobile robot storyboarding: Learning image interest and saliency from pairwise image comparisons,
Abstract: This paper describes a novel storyboarding scheme that uses a model trained
on pairwise image comparisons to identify images likely to be of interest to a
mobile robot user. Traditional storyboarding schemes typically attempt to
summarise robot observations using predefined novelty or image quality
objectives, but we propose a user training stage that allows the incorporation
of user interest when storyboarding. Our approach dramatically reduces the
number of image comparisons required to infer image interest by applying a
Gaussian process smoothing algorithm on image features extracted using a
pre-trained convolutional neural network. As a particularly valuable
by-product, the proposed approach allows the generation of user-specific
saliency or attention maps.