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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ren, R. Jose, J.M. |
| Copyright Year | 2009 |
| Abstract | This paper exploits a media document representation called feature terms to generate a query from multiple media examples, e.g. images. A feature term denotes a continuous interval of a media feature dimension. This approach (1) helps feature accumulation from multiple examples; (2) enables the exploration of text-based retrieval models for multimedia retrieval. Three criteria, minimised χ2, minimised AC/DC and maximised entropy, are proposed to optimise feature term selection. Two ranking functions, KL divergence and BM25, are used for relevance estimation. Experiments on Corel photo collection and TRECVid 2006 collection show the effectiveness in image/video retrieval. |
| Starting Page | 138 |
| Ending Page | 143 |
| File Size | 388890 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424442652 |
| DOI | 10.1109/CBMI.2009.13 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-03 |
| Publisher Place | Greece |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Image retrieval Multimedia computing aggregation model Information retrieval Entropy feature term multimedia retrieval Employment Fusion power generation Character generation Machine learning Feature extraction query generation Indexing |
| Content Type | Text |
| Resource Type | Article |
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