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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Srihari, R.K. Rao, A. Han, B. Munirathnam, S. Xiaoyun Wu |
| Copyright Year | 2000 |
| Description | Author affiliation: State Univ. of New York, Buffalo, NY, USA (Srihari, R.K.) |
| Abstract | Finding useful information from large multimodal document collections such as the WWW without encountering numerous false positives poses a challenge to multimodal information retrieval systems (MMIR). A general model for multimodal information retrieval is proposed by which a user's information need is expressed through composite, multimodal queries, and the most appropriate weighted combination of indexing techniques is determined by a machine learning approach in order to best satisfy the information need. The focus is on improving precision and recall in a MMIR system by optimally combining text and image similarity. Experiments are presented which demonstrate the utility of individual indexing systems in improving overall average precision. |
| Starting Page | 701 |
| Ending Page | 704 |
| File Size | 399382 |
| Page Count | 4 |
| File Format | |
| ISBN | 0780365364 |
| DOI | 10.1109/ICME.2000.871458 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2000-07-30 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Information retrieval Indexing Image retrieval Content based retrieval Database languages Feedback Utility theory World Wide Web Machine learning Logic |
| Content Type | Text |
| Resource Type | Article |
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