Title

Multi-View Manifold Learning For Media Interestingness Prediction

Keywords

Media interestingness analysis; Multi-view manifold learning

Abstract

Media interestingness prediction plays an important role in many real-world applications and attracts much research attention recently. In this paper, we aim to investigate this problem from the perspective of supervised feature extraction. Specifically, we design a novel algorithm dubbed Multi-view Manifold Learning (M2L) to uncover the latent factors that are capable of distinguishing interesting media data from non-interesting ones. By modelling both geometry preserving criterion and discrimination maximization criterion in a unified framework, M2L learns a common subspace for data from multiple views. The analytical solution of M2 L is obtained by solving a generalized eigen-decomposition problem. Experiments on the Predicting Media Interestingness Dataset validate the effectiveness of the proposed method.

Publication Date

6-6-2017

Publication Title

ICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval

Number of Pages

308-314

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1145/3078971.3079021

Socpus ID

85021816539 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85021816539

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