A framework of query expansion for image retrieval based on knowledge base and concept similarity

Yuanfeng He; Yuanxi Li; Jiajia Lei; C.H.C Leung

doi:10.1016/j.neucom.2015.11.102

A framework of query expansion for image retrieval based on knowledge base and concept similarity

Yuanfeng He^*
, Yuanxi Li
, Jiajia Lei
, C.H.C Leung

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

25 Citations (Scopus)

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.

Original language	English
Pages (from-to)	26-32
Number of pages	7
Journal	Neurocomputing
Volume	204
DOIs	https://doi.org/10.1016/j.neucom.2015.11.102
Publication status	Published - 5 Sept 2016

User-Defined Keywords

Image retrieval
Knowledge base
Query expansion

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10.1016/j.neucom.2015.11.102

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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. ",

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doi = "10.1016/j.neucom.2015.11.102",

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T1 - A framework of query expansion for image retrieval based on knowledge base and concept similarity

AU - He, Yuanfeng

AU - Li, Yuanxi

AU - Lei, Jiajia

AU - Leung, C.H.C

N1 - 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: © 2016 Published by Elsevier B.V.

PY - 2016/9/5

Y1 - 2016/9/5

N2 - 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.

AB - 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.

KW - Image retrieval

KW - Knowledge base

KW - Query expansion

UR - https://www.scopus.com/pages/publications/84973100740

U2 - 10.1016/j.neucom.2015.11.102

DO - 10.1016/j.neucom.2015.11.102

M3 - Journal article

AN - SCOPUS:84973100740

SN - 0925-2312

VL - 204

SP - 26

EP - 32

JO - Neurocomputing

JF - Neurocomputing

ER -

A framework of query expansion for image retrieval based on knowledge base and concept similarity

Abstract

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