A framework of query expansion for image retrieval based on knowledge base and concept similarity-Reference-Cited by-同舟云学术

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

Published:2016-09 Issue: Volume:204 Page:26-32
ISSN:0925-2312
Container-title:Neurocomputing
language:en
Short-container-title:Neurocomputing

Author:

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

Funder

National Natural Science Foundation of China

Hubei Province Science and Technology Support Program

Shenzhen Research Council

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience,Computer Science Applications

Reference36 articles.

1. Y. LI, Semantic image similarity based on deep knowledge for effective image retrieval, (Ph.D. thesis), Hong Kong Baptist University, (August 2014).

2. A survey of automatic query expansion in information retrieval;Carpineto;ACM Comput. Surv.,2012

3. T. Takagi, M. Tajima, Query expansion using conceptual fuzzy sets for search engine, in: Proceedings of the 10th IEEE International Conference on Fuzzy Systems, vol. 3, 2001, pp. 1303–1308.

4. Ontology driven query expansion for better image retrieval;Gulati;Int. J. Comput. Appl.,2010

5. Image retrieval from the world wide web issues, techniques, and systems;Kher;ACM Comput. Surv.,2004

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