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A Discriminative Latent Model of Object Classes and Attributes

  • Yang Wang
  • Greg Mori
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6315)

Abstract

We present a discriminatively trained model for joint modelling of object class labels (e.g. “person”, “dog”, “chair”, etc.) and their visual attributes (e.g. “has head”, “furry”, “metal”, etc.). We treat attributes of an object as latent variables in our model and capture the correlations among attributes using an undirected graphical model built from training data. The advantage of our model is that it allows us to infer object class labels using the information of both the test image itself and its (latent) attributes. Our model unifies object class prediction and attribute prediction in a principled framework. It is also flexible enough to deal with different performance measurements. Our experimental results provide quantitative evidence that attributes can improve object naming.

Keywords

Training Data Object Class Object Category Visual Attribute Class Accuracy 
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 2010

Authors and Affiliations

  • Yang Wang
    • 1
  • Greg Mori
    • 1
  1. 1.School of Computing ScienceSimon Fraser UniversityCanada

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