@article{0c5fcebc39d4495c959b8f8bf042a46b,
title = "A framework of query expansion for image retrieval based on knowledge base and concept similarity",
abstract = "We study several semantic concept-based query expansion and re-ranking scheme and compare different ontology-based expansion methods in image search and retrieval. To improve the query expansion efficiency and accuracy, we employ the CYC knowledge base to generate the expansion candidate concepts, while filter and rank the expansion results by calculating concept similarities using the Semantic Relatedness Metrics. Using our knowledge-based query expansion in image retrieval, the efficiency and accuracy has been improved.",
keywords = "Image retrieval, Knowledge base, Query expansion",
author = "Yuanfeng He and Yuanxi Li and Jiajia Lei and C.H.C Leung",
note = "Funding information: The authors would like to thank the handling associate editor and all anonymous reviewers for their positive support and constructive comments for improving the quality of this paper. This work was supported in part by the National Natural Science Foundation of China under Grant 61272203, in part by the Hubei Province Science and Technology Support Program under Grant 2013BAA120, and in part by the Shenzhen Research Council under Grant JCYJ20140819154343378. Publisher Copyright: {\textcopyright} 2016 Published by Elsevier B.V. ",
year = "2016",
month = sep,
day = "5",
doi = "10.1016/j.neucom.2015.11.102",
language = "English",
volume = "204",
pages = "26--32",
journal = "Neurocomputing",
issn = "0925-2312",
publisher = "Elsevier B.V.",
}