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

Abstract : 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 selection
Complete list of metadatas

https://hal.inria.fr/hal-01858385
Contributor : Team Perception <>
Submitted on : Monday, August 20, 2018 - 3:47:58 PM
Last modification on : Thursday, September 13, 2018 - 10:07:56 AM

Links full text

Identifiers

Collections

Citation

Aliaksandr Siarohin, Gloria Zen, Cveta Majtanovic, Xavier Alameda-Pineda, Elisa Ricci, et al.. How to Make an Image More Memorable? A Deep Style Transfer Approach. ICMR 2017 - ACM International Conference on Multimedia Retrieval, Jun 2017, Bucharest, Romania. pp.322-329, ⟨10.1145/3078971.3078986⟩. ⟨hal-01858385⟩

Share

Metrics

Record views

201