| Title | Supervised manifold learning for media interestingness prediction |
| Creator | |
| Date Issued | 2016 |
| Source Publication | CEUR Workshop Proceedings
 |
| ISSN | 1613-0073 |
| Volume | 1739 |
| Abstract | 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. |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85006320562 |
| Citation statistics | |
| Document Type | Conference paper |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/6423 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Affiliation | 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 |
| Recommended Citation GB/T 7714 | Liu，Yang,Gu，Zhonglei,Cheung，Yiu Ming. Supervised manifold learning for media interestingness prediction[C], 2016. |
| Files in This Item: | There are no files associated with this item. | |||||
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