NDLI: A new multimodal fusion method based on association rules mining for image retrieval

Content Provider	IEEE Xplore Digital Library
Author	Alghamdi, R.A. Taileb, M. Ameen, M.
Copyright Year	2014
Description	Author affiliation: Fac. of Comput. & Inf. Technol., King Abdulaziz Univ., Jeddah, Saudi Arabia (Alghamdi, R.A.; Taileb, M.; Ameen, M.)
Abstract	The retrieving method proposed in this paper utilizes the fusion of the images' multimodal information (textual and visual) which is a recent trend in image retrieval researches. It combines two different data mining techniques to retrieve semantically related images: clustering and association rules mining algorithm. The semantic association rules mining is constructed at the offline phase where the association rules are discovered between the text semantic clusters and the visual clusters of the images to use it later at the online phase. The experiment was conducted on more than 54,500 images of ImageCLEF 2011 Wikipedia collection. It was compared to an online image retrieving system called MMRetrieval and to the proposed system but without using association rules. The obtained results show that the proposed method achieved the best precision score among different query categories.
Sponsorship	IEEE Lebanon Sect.
Starting Page	493
Ending Page	499
File Size	740716
Page Count	7
File Format	PDF
ISBN	9781479923373
DOI	10.1109/MELCON.2014.6820584
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-04-13
Publisher Place	Lebanon
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Visualization Clustering algorithms Feature extraction Itemsets Association rules Semantics Clustering Image retrieval Multimodal information fusion Association rules mining
Content Type	Text
Resource Type	Article

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