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International Journal of Multimedia Information Retrieval

Erschienen in:

01.09.2016 | Regular Paper

Image recommendation based on keyword relevance using absorbing Markov chain and image features

verfasst von: D. Sejal, V. Rashmi, K. R. Venugopal, S. S. Iyengar, L. M. Patnaik

Erschienen in: International Journal of Multimedia Information Retrieval | Ausgabe 3/2016

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Abstract

Image 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.

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Titel: Image recommendation based on keyword relevance using absorbing Markov chain and image features
verfasst von: D. Sejal
V. Rashmi
K. R. Venugopal
S. S. Iyengar
L. M. Patnaik
Publikationsdatum: 01.09.2016
Verlag: Springer London
Erschienen in: International Journal of Multimedia Information Retrieval / Ausgabe 3/2016
Print ISSN: 2192-6611
Elektronische ISSN: 2192-662X
DOI: https://doi.org/10.1007/s13735-016-0104-9

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