In many Web image retrieval applications, adapting the retrieval results according to some model of the user is a desired feature as the returned images can be made specifically relevant to a user’s needs. Making retrieval user-adaptive faces several practical challenges, including the ambiguity of user query, the lack of user-adaptive training data, and lack of proper mechanisms for supporting adaptive learning. To address some of these challenges, we propose a hybrid learning strategy that fuses knowledge from both pointwise and pairwise training data into one framework for attribute-based, user-adaptive image retrieval. An online learning algorithm is developed for updating the ranking performance based on user feedback. The framework is also derived into a kernel form allowing easy application of kernel techniques. We use both synthetic and real-world datasets to evaluate the performance of the proposed algorithm. Comparison with other state-of-the-art approaches suggests that our method achieves obvious performance gains over ranking and zero-shot learning. Further, our online learning algorithm was found to be able to deliver much better performance than batch learning, given the same elapsed running time.

Original languageEnglish (US)
Pages (from-to)19-33
Number of pages15
JournalInternational Journal of Multimedia Information Retrieval
Issue number1
StatePublished - Mar 1 2016


  • Adaptive image retrieval
  • Attribute learning
  • Learning to rank

ASJC Scopus subject areas

  • Information Systems
  • Media Technology
  • Library and Information Sciences


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