Efficient image retrieval using conceptualization of annotated images

Authors:
Miyoung Cho;Chang Choi;Hanil Kim;Jungpil Shin;Pankoo Kim
Affiliations:
Dept. of Computer Science and Engineering, Chosun University, Gwangju, Korea;Dept. of Computer Science and Engineering, Chosun University, Gwangju, Korea;Dept. of Computer Education, Cheju National University, Jeju, Korea;Graduate School of Computer Science and Engineering, Aizu University, Aizu-Wakamatsu City, Fukushima, Japan;Dept. of CSE, Chosun University, Korea
Venue:
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
Year:
2007

Citing 4
Cited 1

An Information-Theoretic Definition of Similarity

ICML '98 Proceedings of the Fifteenth International Conference on Machine Learning
Image annotations by combining multiple evidence & wordNet

Proceedings of the 13th annual ACM international conference on Multimedia
Using information content to evaluate semantic similarity in a taxonomy

IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Extended gloss overlaps as a measure of semantic relatedness

IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence

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

AIKED'12 Proceedings of the 11th WSEAS international conference on Artificial Intelligence, Knowledge Engineering and Data Bases

Quantified Score

Hi-index	0.00

Visualization

Abstract

As the amount of visual information is rapidly increasing, users want to find the more semantic information easily. Most retrieval systems by lowlevel features(such as color, texture) could not satisfy user's demand. To interpret semantic of image, many researchers use keywords as textual annotation. However, it's the image retrieval without ranking by text matching which is the simplest way to retrieval according to keyword's existence or nonexistence. In this paper, we propose conceptualization by similarity measure using relations among keywords for efficient image retrieval. We experiment annotated image retrieval by lowering the unrelated keyword's weight value and raising important keyword's one.