Abstract
Current image retrieval systems still have major difficulties in bridging the gap between high-level concept and low-level image representation. To overcome these difficulties, a memorization learning model is proposed in this paper. It memorizes the semantic knowledge of images in a database by simply accumulating the user-provided relevance feedback information. From the memorized knowledge, it then learns some hidden semantic information of images. Image retrieval is finally based on a seamless combination of low-level features, memorized semantic information, and estimated hidden semantic information. The model is easy to implement and can be efficiently applied to an image retrieval system. Preliminary experimental results on 10,000 images demonstrate the effectiveness of the proposed model.
Original language | English |
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Pages | 605-608 |
Number of pages | 4 |
State | Published - 2003 |
Event | Proceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain Duration: 14 Sep 2003 → 17 Sep 2003 |
Conference
Conference | Proceedings: 2003 International Conference on Image Processing, ICIP-2003 |
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Country/Territory | Spain |
City | Barcelona |
Period | 14/09/03 → 17/09/03 |