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Constrained Semi-Supervised Learning Using Attributes and Comparative Attributes

  • Abhinav Shrivastava
  • Saurabh Singh
  • Abhinav Gupta
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7574)

Abstract

We consider the problem of semi-supervised bootstrap learning for scene categorization. Existing semi-supervised approaches are typically unreliable and face semantic drift because the learning task is under-constrained. This is primarily because they ignore the strong interactions that often exist between scene categories, such as the common attributes shared across categories as well as the attributes which make one scene different from another. The goal of this paper is to exploit these relationships and constrain the semi-supervised learning problem. For example, the knowledge that an image is an auditorium can improve labeling of amphitheaters by enforcing constraint that an amphitheater image should have more circular structures than an auditorium image. We propose constraints based on mutual exclusion, binary attributes and comparative attributes and show that they help us to constrain the learning problem and avoid semantic drift. We demonstrate the effectiveness of our approach through extensive experiments, including results on a very large dataset of one million images.

Keywords

Unlabeled Data Mutual Exclusion Comparative Attribute Pairwise Constraint Scene Category 
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

  • Abhinav Shrivastava
    • 1
  • Saurabh Singh
    • 1
  • Abhinav Gupta
    • 1
  1. 1.The Robotics InstituteCarnegie Mellon UniversityPittsburghUSA

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