Learning memorability preserving subspace for predicting media memorability

Yang Liu, Zhonglei Gu, Tobey H. Ko

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

Abstract

This paper describes our approach designed for the MediaEval 2018 Predicting Media Memorability Task. First, a subspace learning method called Memorability Preserving Embedding (MPE) is proposed to learn discriminative subspace from the original feature space according to the memorability scores. Then the Support Vector Regressor (SVR) is applied to the learned subspace for memorability prediction. The prediction performance demonstrates that SVR can achieve good performance even in a very low-dimensional subspace, which implies that the subspace learned by the MPE is capable of preserving important memorability information. Moreover, the results indicate that the short-term memorability is more predictable than the long-term memorability. Copyright held by the owner/author(s).

Original languageEnglish
Title of host publicationWorking Notes Proceedings of the MediaEval 2018 Workshop
EditorsMartha Larson, Piyush Arora, Claire-Hélène Demarty, Michael Riegler, Benjamin Bischke, Emmanuel Dellandrea, Mathias Lux, Alastair Porter, Gareth J. F. Jones
PublisherCEUR-WS
Number of pages3
Publication statusPublished - Oct 2018
EventMediaEval 2018: Multimedia Benchmark Workshop - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018
https://ceur-ws.org/Vol-2283/

Publication series

NameCEUR Workshop Proceedings
PublisherCEUR-WS
Volume2283
ISSN (Print)1613-0073

Conference

ConferenceMediaEval 2018: Multimedia Benchmark Workshop
Country/TerritoryFrance
CitySophia Antipolis
Period29/10/1831/10/18
Internet address

Scopus Subject Areas

  • Computer Science(all)

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