With the explosion of visual information nowadays, millions of digital images are available to the users. How to efﬁciently explore a large set of images and retrieve useful information thus becomes extremely important. Unfortunately only some of the images can impress the user at ﬁrst glance. Others that make little sense in human perception are often discarded, while still costing valuable time and space. Therefore, it is signiﬁcant to identify these two kinds of images for relieving the load of online repositories and accelerating information retrieval process. However, most of the existing imageproperties,e.g.,memorabilityandpopularity,arebased on repeated human interactions, which limit the research and application of evaluating image quality in terms of instantaneous impression. In this paper, we propose a novel image property, called impressiveness, that measures how images impress people witha short-term contact. This isbased on an impression-driven model inspired by a number of important human perceptual cues. To achieve this, we ﬁrst collect three datasets in various domains, which are labeled according to theinstantaneoussensationoftheannotators.Thenweinvestigate the impressiveness property via six established human perceptual cues as well as the corresponding features from pixel to semantic levels. Sequentially, we verify the consistencyoftheimpressiveness whichcanbequantitativelymeasured by multiple visual representations, and evaluate their latent relationships. Finally, we apply the proposed impressiveness property to rank the images for an efﬁcient image recommendation system.
|Number of pages
|Published - 2018
|AAAI Conference on Artificial Intelligence 2018 -
Duration: 2 Feb 2018 → 7 Feb 2018
|AAAI Conference on Artificial Intelligence 2018
|2/02/18 → 7/02/18