A method for enhancing image retrieval based on annotation using modified WUP similarity in WordNet

Authors:
Dongjin Choi;Jungin Kim;Hayoung Kim;Myunggwon Hwang;Pankoo Kim
Affiliations:
Department of Computer Engineering, Chosun University, Gwangju, Republic of Korea;Department of Computer Engineering, Chosun University, Gwangju, Republic of Korea;Department of Computer Engineering, Chosun University, Gwangju, Republic of Korea;Korea Institute of Science and Technology Information, Daejon, Republic of Korea;Department of Computer Engineering, Chosun University, Gwangju, Republic of Korea
Venue:
AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases
Year:
2012

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Abstract

Images are the most common contents on the Internet for a long time. Lots of researchers have been studied to satisfy user demands for semantic visual recognition using low-level feature (such as color or texture) or keywords which were textual annotations but still challenging. Keywords in images give great evidence to identify what images are. Keywords are not always related with image its own. It is necessary to remove those irrelevant keywords and give higher values to relevant keywords using statistical models and knowledge base such as WordNet. For this reason, we propose a modified WUP similarity measurement in WordNet to decide which keywords are close to image. To identify irrelevant keywords, we use various semantic similarity measures between keywords and image titles. We focus on solving word sense disambiguation of image titles (such as bat, mouse, jaguar, etc). The results show that by augmenting knowledge-based with proposed method we can remove irrelevant images and take a further step to solve the WSD problem.