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

  • D. Sejal
  • V. Rashmi
  • K. R. Venugopal
  • S. S. Iyengar
  • L. M. Patnaik
Regular Paper


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.


Annotation-based image retrieval Content-based image retrieval Image annotation Image recommendation 


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Copyright information

© Springer-Verlag London 2016

Authors and Affiliations

  • D. Sejal
    • 1
  • V. Rashmi
    • 1
  • K. R. Venugopal
    • 1
  • S. S. Iyengar
    • 2
  • L. M. Patnaik
    • 3
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of Engineering, Bangalore UniversityBengaluruIndia
  2. 2.Florida International UniversityMiamiUSA
  3. 3.National Institute of Advanced StudiesBengaluruIndia

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