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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Jie Chen Jianlong Tan Xiangzhou Yin Hao Liao |
| Copyright Year | 2010 |
| Description | Author affiliation: Beijing University of Posts and Telecommunication, Beijing, China (Jie Chen) || Institute of Computing Technology Chinese Academy of Sciences, Beijing, China (Jianlong Tan; Xiangzhou Yin; Hao Liao) |
| Abstract | In Web 2.0 applications, users always label digital images using textual descriptions, which are also called tags. As a result, a web image usually carries both tag and visual content information. In order to improve the retrieval performance of web images, in this paper, we propose an error-driven fusion co-clustering algorithm, which combines images' tags, visual contents together for analysis. Experimental results demonstrate that our algorithm outperforms other simple clustering methods. |
| Starting Page | 378 |
| Ending Page | 382 |
| File Size | 134600 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781424468515 |
| e-ISBN | 9781424468539 |
| DOI | 10.1109/ICNIDC.2010.5657793 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-09-24 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Visualization Clustering methods Semantics Image retrieval Clustering algorithms Feature extraction Bipartite graph Partitioning algorithms Error-driven fusion Co-clustering |
| Content Type | Text |
| Resource Type | Article |
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