Relevance feedback and latent semantic index based cultural relic image retrieval

Authors:
Na Wei;Ming-Quan Zhou;Guo-Hua Geng
Affiliations:
College of Information Engineering, Chang'an University, Xi'an, Shaanxi, China;College of Information Science and Technology, Beijing Normal University, Beijing, China;College of Information Science and Technology, Northwest University, Xi'an, China
Venue:
Edutainment'07 Proceedings of the 2nd international conference on Technologies for e-learning and digital entertainment
Year:
2007

Citing 5
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Abstract

In this paper we present a novel relevance feedback and latent semantic index based cultural relic image retrieval system. First, the optimum weights that can be used for iterative retrieval is computed, then a semantic image link network is constructed to store the semantic correlation information between images, which is obtained from memorized relevance feedbacks. Following image relevance feedback, Latent semantic indexing is applied to image retrieval, which helps saving in storage and estimating the hidden semantic relationship among images. To illustrate the potential of such an approach a prototype image retrieval system has been developed and Preliminary experimental results on a database containing about 2000 images demonstrate the effectiveness of the proposed model.