Large-scale cross-media retrieval of WikipediaMM images with textual and visual query expansion

Authors:
Zhi Zhou;Yonghong Tian;Yuanning Li;Tiejun Huang;Wen Gao
Affiliations:
Key Lab. of Intelligent Inf. Processing, Inst. of Computing Techn., Chinese Academy of Sci., Beijing, China and Graduate Univ. of Chinese Academy of Sci., Beijing, China and Inst. of Digital Media ...;Institute of Digital Media, School of EE & CS, Peking University, Beijing, China;Key Lab. of Intelligent Inf. Processing, Inst. of Computing Techn., Chinese Academy of Sci., Beijing, China and Graduate Univ. of Chinese Academy of Sci., Beijing, China and Inst. of Digital Media ...;Institute of Digital Media, School of EE & CS, Peking University, Beijing, China;Institute of Digital Media, School of EE & CS, Peking University, Beijing, China
Venue:
CLEF'08 Proceedings of the 9th Cross-language evaluation forum conference on Evaluating systems for multilingual and multimodal information access
Year:
2008

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Abstract

In this paper, we present our approaches for the WikipediaMM task at ImageCLEF 2008. We first experimented with a text-based image retrieval approach with query expansion, where the extension terms were automatically selected from a knowledge base that was semi-automatically constructed from Wikipedia. Encouragingly, the experimental results rank in the first place among all submitted runs. We also implemented a content-based image retrieval approach with query-dependent visual concept detection. Then cross-media retrieval was successfully carried out by independently applying the two metasearch tools and then combining the results through a weighted summation of scores. Though not submitted, this approach outperforms our text-based and content-based approaches remarkably.