Personalized Multimedia Retrieval in CADAL Digital Library

Authors:
Yin Zhang;Jiangqin Wu;Yueting Zhuang
Affiliations:
College of Computer Science, Zhejiang University, Hangzhou, China 310027;College of Computer Science, Zhejiang University, Hangzhou, China 310027;College of Computer Science, Zhejiang University, Hangzhou, China 310027
Venue:
PCM '08 Proceedings of the 9th Pacific Rim Conference on Multimedia: Advances in Multimedia Information Processing
Year:
2008

Citing 4
Cited 0

Towards Data-Adaptive and User-Adaptive Image Retrieval by Peer Indexing

International Journal of Computer Vision - Special Issue on Content-Based Image Retrieval
Random-Walk Computation of Similarities between Nodes of a Graph with Application to Collaborative Recommendation

IEEE Transactions on Knowledge and Data Engineering
Personalized multimedia retrieval: the new trend?

Proceedings of the international workshop on Workshop on multimedia information retrieval
Mining Semantic Correlation of Heterogeneous Multimedia Data for Cross-Media Retrieval

IEEE Transactions on Multimedia

Quantified Score

Hi-index	0.00

Visualization

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.