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2016 International Conference on Big Data and Smart Computing (BigComp) (2016)
Hong Kong, China
Jan. 18, 2016 to Jan. 20, 2016
ISSN: 2375-9356
ISBN: 978-1-4673-8795-8
pp: 191-198
Nguyen Anh Tu , Department of Computer Engineering, Kyung Hee University, Korea
Jinsung Cho , Department of Computer Engineering, Kyung Hee University, Korea
Young-Koo Lee , Department of Computer Engineering, Kyung Hee University, Korea
ABSTRACT
Social image search becomes an active research field in recent years due to the rapid development in big data processing technologies. In the retrieval systems, text description/tags play a key role to bridge the semantic gap between low-level features and higher-level concepts, and so guarantee the reliable search. However, in practice manual tags are usually noisy and incomplete, resulting in a limited performance of image retrieval. To tackle this problem, we propose a probabilistic topic model to formalize the correlation of tags with visual features via the latent semantic topics. Our proposed approach allows us to effectively annotate and refine tags based on a Monte Carlo Markov Chain algorithm for approximate inference. Moreover, we present a measuring scheme using the refined tags and extracted topics for ranking the images. The experimental results from two large benchmark datasets show that our approach provides promising accuracy.
INDEX TERMS
Visualization, Semantics, Image retrieval, Data models, Vocabulary, Probabilistic logic
CITATION

Nguyen Anh Tu, Jinsung Cho and Young-Koo Lee, "Semantic image retrieval using correspondence topic model with background distribution," 2016 International Conference on Big Data and Smart Computing (BigComp)(BIGCOMP), Hong Kong, China, 2016, pp. 191-198.
doi:10.1109/BIGCOMP.2016.7425820
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