ACSIR: ANOVA Cosine Similarity Image Recommendation in vertical search

  • D. SejalEmail author
  • T. Ganeshsingh
  • K. R. Venugopal
  • S. S. Iyengar
  • L. M. Patnaik
Regular Paper


In today’s world, online shopping is very attractive and grown exponentially due to revolution in digitization. It is a crucial demand to provide recommendation for all the search engine to identify users’ need. In this paper, we have proposed a ANOVA Cosine Similarity Image Recommendation (ACSIR) framework for vertical image search where text and visual features are integrated to fill the semantic gap. Visual synonyms of each term are computed using ANOVA p value by considering image visual features on text-based search. Expanded queries are generated for user input query, and text-based search is performed to get the initial result set. Pair-wise image cosine similarity is computed for recommendation of images. Experiments are conducted on product images crawled from domain-specific site. Experiment results show that the ACSIR outperforms iLike method by providing more relevant products to the user input query.


Content-based image retrieval (CBIR) Image recommendation Vertical search 


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Copyright information

© Springer-Verlag London 2017

Authors and Affiliations

  • D. Sejal
    • 1
    • 2
    Email author
  • T. Ganeshsingh
    • 1
  • K. R. Venugopal
    • 1
  • S. S. Iyengar
    • 3
  • L. M. Patnaik
    • 4
  1. 1.Department of Computer Science and EngineeringUniversity Visvesvaraya College of Engineering, Bangalore UniversityBangaloreIndia
  2. 2.T. John Institute of TechnologyBangaloreIndia
  3. 3.Florida International UniversityMiamiUSA
  4. 4.National Institute of Advanced StudiesBangaloreIndia

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