Supervised manifold learning for media interestingness prediction

科研成果详情

题名	Supervised manifold learning for media interestingness prediction
作者	Liu，Yang 1,2; Gu，Zhonglei 3; Cheung，Yiu Ming 1,2,4
发表日期	2016
会议录名称	CEUR Workshop Proceedings
ISSN	1613-0073
卷号	1739
摘要	In this paper, we describe the models designed for automatically selecting multimedia data, e.g., image and video segments, which are considered to be interesting for a common viewer. Specifically, we utilize an existing dimensionality reduction method called Neighborhood MinMax Projections (NMMP) to extract the low-dimensional features for predicting the discrete interestingness labels. Meanwhile, we introduce a new dimensionality reduction method dubbed Supervised Manifold Regression (SMR) to learn the compact representations for predicting the continuous interestingness levels. Finally, we use the nearest neighbor classifier and support vector regressor for classification and regression, respectively. Experimental results demonstrate the effectiveness of the low-dimensional features learned by NMMP and SMR.
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语种	英语English
Scopus入藏号	2-s2.0-85006320562
引用统计
文献类型	会议论文
条目标识符	https://repository.uic.edu.cn/handle/39GCC9TT/6423
专题	北师香港浸会大学
作者单位	1.Department of Computer Science,Hong Kong Baptist University,Kowloon Tong,Hong Kong 2.Institute of Research and Continuing Education,Hong Kong Baptist University,Shenzhen,China 3.AAOO Tech Limited,Shatin,Hong Kong 4.United International College,Beijing Normal University,Hong Kong Baptist University,Zhuhai,China
推荐引用方式 GB/T 7714	Liu，Yang,Gu，Zhonglei,Cheung，Yiu Ming. Supervised manifold learning for media interestingness prediction[C], 2016.