Efficient Thumbnail Identification through Object Recognition

Salvatore Carta, Eugenio Gaeta, Alessandro Giuliani, Leonardo Piano, Diego Recupero

2020

Abstract

Given the overwhelming growth of online videos, providing suitable video thumbnails is important not only to influence user’s browsing and searching experience, but also for companies involved in exploiting video sharing portals (YouTube, in our work) for their business activities (e.g., advertising). A main requirement for automated thumbnail generation frameworks is to be highly reliable and time-efficient, and, at the same time, economic in terms of computational efforts. As conventional methods often fail to produce satisfying results, video thumbnail generation is a challenging research topic. In this paper, we propose two novel approaches able to provide relevant thumbnails with the minimum effort in terms of time execution and computational resources. The proposals rely on an object recognition framework which captures the most topic-related frames of a video, and selects the thumbnails from its resulting frames set. Our approach is a trade-off between content-coverage and time-efficiency. We perform preliminary experiments aimed at assessing and validating our models, and we compare them with a baseline compliant to the state-of-the-art. The assessments confirm our expectations, and encourage the future improvement of the proposed algorithms, as our proposals are significantly faster and more accurate than the baseline.

Paper Citation

in Harvard Style

Carta S., Gaeta E., Giuliani A., Piano L. and Recupero D. (2020). Efficient Thumbnail Identification through Object Recognition.In Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST, ISBN 978-989-758-478-7, pages 209-216. DOI: 10.5220/0010108802090216

in Bibtex Style

@conference{webist20,
author={Salvatore Carta and Eugenio Gaeta and Alessandro Giuliani and Leonardo Piano and Diego Recupero},
title={Efficient Thumbnail Identification through Object Recognition},
booktitle={Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,},
year={2020},
pages={209-216},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010108802090216},
isbn={978-989-758-478-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 16th International Conference on Web Information Systems and Technologies - Volume 1: WEBIST,
TI - Efficient Thumbnail Identification through Object Recognition
SN - 978-989-758-478-7
AU - Carta S.
AU - Gaeta E.
AU - Giuliani A.
AU - Piano L.
AU - Recupero D.
PY - 2020
SP - 209
EP - 216
DO - 10.5220/0010108802090216