Personalized multimedia retrieval in CADAL digital library

Abstract

CADAL has been a large digital library including million digital books and large volumes of multimedia resources, e.g. videos, images. In this paper, in order to overcome the problem of information overload, we propose a framework of personalized cross-media retrieval in CADAL digital library and present the details of the algorithms used in the personalized cross-media retrieval, which is a new kind of retrieval technology by which query examples and search results can be of different modalities. In order to provide personalized cross-media retrieval, we construct the uniform cross-media correlation graph in terms of three kinds of information: low-level features of media objects, co-existence information between them and semantic correlations between MMDs that are mined out of large amounts of logs. Moreover, we also use the in-session relevance feedback approach to mine the hints in the positive and negative examples to boost the retrieval performance for the individuals. © 2008 Springer.