Predicting memorability of images using attention-driven spatial pooling and image semantics

In daily life, humans demonstrate an amazing ability to remember images they see on magazines, commercials, TV, web pages, etc. but automatic prediction of intrinsic memorability of images using computer vision and machine learning techniques has only been investigated very recently. Our goal in this article is to explore the role of visual attention and image semantics in understanding image memorability. In particular, we present an attention-driven spatial pooling strategy and show that considering image features from the salient parts of images improves the results of the previous models. We also investigate different semantic properties of images by carrying out an analysis of a diverse set of recently proposed semantic features which encode meta-level object categories, scene attributes, and invoked feelings. We show that these features which are automatically extracted from images provide memorability predictions as nearly accurate as those derived from human annotations. Moreover, our combined model yields results superior to those of state-of-the art fully automatic models. We examine the role of visual attention and image semantics in understanding image memorability.We propose an attention-driven spatial pooling strategy for image memorability.Considering image features from the salient parts of images improves the results of the previous models.We also investigate different semantic properties of images.Combining attention-driven pooling with semantic features yields state-of-the-art results.