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| Content Provider | ACM Digital Library |
|---|---|
| Author | Li, Xirong Snoek, Cees G.M. |
| Abstract | Automatic visual categorization is critically dependent on labeled examples for supervised learning. As an alternative to traditional expert labeling, social-tagged multimedia is becoming a novel yet subjective and inaccurate source of learning examples. Different from existing work focusing on collecting positive examples, we study in this paper the potential of substituting social tagging for expert labeling for creating negative examples. We present an empirical study using 6.5 million Flickr photos as a source of social tagging. Our experiments on the PASCAL VOC challenge 2008 show that with a relative loss of only 4.3% in terms of mean average precision, expert-labeled negative examples can be completely replaced by social-tagged negative examples for consumer photo categorization. |
| Starting Page | 661 |
| Ending Page | 664 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781605586083 |
| DOI | 10.1145/1631272.1631382 |
| Language | English |
| Publisher | Association for Computing Machinery (ACM) |
| Publisher Date | 2009-10-23 |
| Publisher Place | New York |
| Access Restriction | Subscribed |
| Subject Keyword | Visual categorization Social tagging Negative examples |
| Content Type | Text |
| Resource Type | Article |
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