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A Latent Variable Ranking Model for Content-Based Retrieval

  • Ariadna Quattoni
  • Xavier Carreras
  • Antonio Torralba
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7224)

Abstract

Since their introduction, ranking SVM models [11] have become a powerful tool for training content-based retrieval systems. All we need for training a model are retrieval examples in the form of triplet constraints, i.e. examples specifying that relative to some query, a database item a should be ranked higher than database item b. These types of constraints could be obtained from feedback of users of the retrieval system. Most previous ranking models learn either a global combination of elementary similarity functions or a combination defined with respect to a single database item. Instead, we propose a “coarse to fine” ranking model where given a query we first compute a distribution over “coarse” classes and then use the linear combination that has been optimized for queries of that class. These coarse classes are hidden and need to be induced by the training algorithm. We propose a latent variable ranking model that induces both the latent classes and the weights of the linear combination for each class from ranking triplets. Our experiments over two large image datasets and a text retrieval dataset show the advantages of our model over learning a global combination as well as a combination for each test point (i.e. transductive setting). Furthermore, compared to the transductive approach our model has a clear computational advantages since it does not need to be retrained for each test query.

Keywords

Latent Classis Query Image Ranking Model Local Learning Hinge Loss 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ariadna Quattoni
    • 1
  • Xavier Carreras
    • 1
  • Antonio Torralba
    • 2
  1. 1.Dept. LSIUniversitat Politècnica de CatalunyaBarcelonaSpain
  2. 2.Massachusetts Institute of Technology, CSAILCambridgeUSA

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