Abstract
An image retrieval system based on an information embedding scheme is proposed. Using relevance feedback, the system gradually embeds correlations between images from a high-level semantic perspective. The system starts with low-level image features and acquires knowledge from users to correlate different images in the database. Through the selection of positive and negative examples based on a given query, the semantic relationships between images are captured and embedded into the system by splitting/merging image clusters and updating the correlation matrix. Image retrieval is then based on the resulting image clusters and the correlation matrix obtained through relevance feedback.
| Original language | English |
|---|---|
| Pages (from-to) | 294-304 |
| Journal | Proceedings of SPIE - The International Society for Optical Engineering |
| Volume | 3846 |
| Publication status | Published - 1999 |
| Externally published | Yes |
| Event | Proceedings of the 1999 Multimedia Storage and Archiving Systems IV - Boston, MA, USA Duration: 20 Sept 1999 → 22 Sept 1999 |
Bibliographical note
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