TY - GEN
T1 - Multi-view manifold learning for media interestingness prediction
AU - LIU, Yang
AU - Gu, Zhonglei
AU - CHEUNG, Yiu Ming
AU - Hua, Kien A.
N1 - Funding Information:
The authors would like to thank the reviewers for their constructive comments and suggestions. This work was supported in part by the National Natural Science Foundation of China under Grants 61503317 and 61672444, in part by the Faculty Research Grant of Hong Kong Baptist University (HKBU) under Project FRG2/16-17/032, in part by the Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation (No. JTKF2017001), Changsha University of Science & Technology, P.R. China, and in part by the Science and Technology Research and Development Fund of Shenzhen with Pro ject Code J-CYJ20160531194006833.
PY - 2017/6/6
Y1 - 2017/6/6
N2 - 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.
AB - 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.
KW - Media interestingness analysis
KW - Multi-view manifold learning
UR - http://www.scopus.com/inward/record.url?scp=85021816539&partnerID=8YFLogxK
U2 - 10.1145/3078971.3079021
DO - 10.1145/3078971.3079021
M3 - Conference proceeding
AN - SCOPUS:85021816539
T3 - ICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval
SP - 308
EP - 314
BT - ICMR 2017 - Proceedings of the 2017 ACM International Conference on Multimedia Retrieval
PB - Association for Computing Machinery (ACM)
T2 - 7th ACM International Conference on Multimedia Retrieval, ICMR 2017
Y2 - 6 June 2017 through 9 June 2017
ER -