Estimating Missing Features to Improve Multimedia Information Retrieval
Retrieval in a multimedia database usually involves combining information from different modalities of data, such as text and images. However, all modalities of the data may not be available to form the query. The retrieval results from such a partial query are often less than satisfactory. In this paper, we present an approach to complete a partial query by estimating the missing features in the query. Our experiments with a database of images and their associated captions show that, with an initial text-only query, our completion method has similar performance to a full query with both image and text features. In addition, when we use relevance feedback, our approach outperforms the results obtained using a full query.
- Research Organization:
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- W-7405-ENG-48
- OSTI ID:
- 907852
- Report Number(s):
- UCRL-CONF-225087; TRN: US200721%%425
- Resource Relation:
- Conference: Presented at: Seventh SIAM International Conference on Data Mining, Minneapolis, MN, United States, Apr 26 - Apr 28, 2007
- Country of Publication:
- United States
- Language:
- English
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