How to Make an Image More Memorable? A Deep Style Transfer Approach

Recent works have shown that it is possible to automatically predict intrinsic image properties like memorability. In this paper, we take a step forward addressing the question: "Can we make an image more memorable?". Methods for automatically increasing image memorability would have an impact in many application fields like education, gaming or advertising. Our work is inspired by the popular editing-by-applying-filters paradigm adopted in photo editing applications, like Instagram and Prisma. In this context, the problem of increasing image memorability maps to that of retrieving "memorabilizing" filters or style "seeds". Still, users generally have to go through most of the available filters before finding the desired solution, thus turning the editing process into a resource and time consuming task. In this work, we show that it is possible to automatically retrieve the best style seeds for a given image, thus remarkably reducing the number of human attempts needed to find a good match. Our approach leverages from recent advances in the field of image synthesis and adopts a deep architecture for generating a memorable picture from a given input image and a style seed. Importantly, to automatically select the best style a novel learning-based solution, also relying on deep models, is proposed. Our experimental evaluation, conducted on publicly available benchmarks, demonstrates the effectiveness of the proposed approach for generating memorable images through automatic style seed selectionComment: Accepted at ACM ICMR 201

The pictures we like are our image: continuous mapping of favorite pictures into self-assessed and attributed personality traits

Flickr allows its users to tag the pictures they like as “favorite”. As a result, many users of the popular photo-sharing platform produce galleries of favorite pictures. This article proposes new approaches, based on Computational Aesthetics, capable to infer the personality traits of Flickr users from the galleries above. In particular, the approaches map low-level features extracted from the pictures into numerical scores corresponding to the Big-Five Traits, both self-assessed and attributed. The experiments were performed over 60,000 pictures tagged as favorite by 300 users (the PsychoFlickr Corpus). The results show that it is possible to predict beyond chance both self-assessed and attributed traits. In line with the state-of-the art of Personality Computing, these latter are predicted with higher effectiveness (correlation up to 0.68 between actual and predicted traits)

The modularity of aesthetic processing and perception in the human brain. Functional neuroimaging studies of neuroaesthetics.

By taking advantage of the advent of functional Magnetic Resonance Imaging (fMRI) this thesis argues that aesthetics belongs in the domain of neurobiology by investigating the different brain processes that are implicated in aesthetic perception from two perspectives. The first experiment explores a specific artistic style that has stressed the problem in the relationship between objects and context. This study investigates the neural responses associated with changes in visual perception, as when objects are placed in their normal context versus when the object-context relationship is violated. Indeed, an aim of this study was to cast a new light on this specific artistic style from a neuroscientific perspective. In contrast to basic rewards, which relate to the reproduction of the species, the evolution of abstract, cognitive representations facilitates the use of a different class of rewards related to hedonics. The second part investigates the hedonic processes involved in aesthetic judgments in order to explore if such higher order cognitive rewards use the same neural reward mechanism as basic rewards. In the first of these experiments we modulate the extent to which the neural correlates of aesthetic preference vary as a function of expertise in architecture. In the second experiment we aim to measure the more general effects of labelling works of art with cognitive semantic information in order to explore the neural modulation of aesthetic preference relative to this information. The main finding of this thesis is that stimulus affective value is represented separately in OFC, with positive reward (increasing aesthetic judgments) being represented in medial OFC and negative reward value is being represented in lateral OFC. Furthermore ventral striatum encode reward expectancy and the predictive value of a stimulus. These findings suggest a dissociation of reward processing with separate neural substrates in reward expectancy and stimulus affective value