mage Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features

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Sejal, D. and Rashmi, V. and Venugopal, R.K. and Iyengar, S.S. and Patnaik, L.M. (2016) mage Recommendation Based on Keyword Relevance Using Absorbing Markov Chain and Image Features. International Journal of Multimedia Information Retrieval, vol 5 (No 3,). pp. 185-199. ISSN 2192-662X

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Official URL: https://doi.org/10.1007/s13735-016-0104-9

Abstract

mage recommendation is an important feature of search engine, as tremendous amount of images are available online. It is necessary to retrieve relevant images to meet the user's requirement. In this paper, we present an algorithm image recommendation with absorbing Markov chain (IRAbMC) to retrieve relevant images for a user's input query. Images are ranked by calculating keyword relevance probability between annotated keywords from log and keywords of user input query. Keyword relevance is computed using absorbing Markov chain. Images are reranked using image visual features. Experimental results show that the IRAbMC algorithm outperforms Markovian semantic indexing (MSI) method with improved relevance score of retrieved ranked images.

Item Type: Article
Subjects: Faculty of Engineering > Computer Science & Information Science Engineering
Divisions: University Visvesvarayya College of Engineering > Department of Computer Science and Information Science Engineering
Depositing User: Mrs Sushma H R
Date Deposited: 05 Aug 2016 11:16
Last Modified: 05 Aug 2016 11:16
URI: http://eprints-bangaloreuniversity.in/id/eprint/4217

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