Learning memorability preserving subspace for predicting media memorability

Yang LIU, Zhonglei Gu, Tobey H. Ko

Research output: Contribution to journalConference articlepeer-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
JournalCEUR Workshop Proceedings
Volume2283
Publication statusPublished - 2018
Event2018 Working Notes Proceedings of the MediaEval Workshop, MediaEval 2018 - Sophia Antipolis, France
Duration: 29 Oct 201831 Oct 2018

Scopus Subject Areas

  • Computer Science(all)

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