A memorization learning model for image retrieval

Junwei Han, Mingjing Li, Hongjiang Zhang, Lei Guo

Research output: Contribution to conferencePaperpeer-review

11 Scopus citations

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 languageEnglish
Pages605-608
Number of pages4
StatePublished - 2003
EventProceedings: 2003 International Conference on Image Processing, ICIP-2003 - Barcelona, Spain
Duration: 14 Sep 200317 Sep 2003

Conference

ConferenceProceedings: 2003 International Conference on Image Processing, ICIP-2003
Country/TerritorySpain
CityBarcelona
Period14/09/0317/09/03

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