Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers

  • Abhinav Gupta
  • Larry S. Davis
Conference paper

DOI: 10.1007/978-3-540-88682-2_3

Part of the Lecture Notes in Computer Science book series (LNCS, volume 5302)
Cite this paper as:
Gupta A., Davis L.S. (2008) Beyond Nouns: Exploiting Prepositions and Comparative Adjectives for Learning Visual Classifiers. In: Forsyth D., Torr P., Zisserman A. (eds) Computer Vision – ECCV 2008. ECCV 2008. Lecture Notes in Computer Science, vol 5302. Springer, Berlin, Heidelberg

Abstract

Learning visual classifiers for object recognition from weakly labeled data requires determining correspondence between image regions and semantic object classes. Most approaches use co-occurrence of “nouns” and image features over large datasets to determine the correspondence, but many correspondence ambiguities remain. We further constrain the correspondence problem by exploiting additional language constructs to improve the learning process from weakly labeled data. We consider both “prepositions” and “comparative adjectives” which are used to express relationships between objects. If the models of such relationships can be determined, they help resolve correspondence ambiguities. However, learning models of these relationships requires solving the correspondence problem. We simultaneously learn the visual features defining “nouns” and the differential visual features defining such “binary-relationships” using an EM-based approach.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Abhinav Gupta
    • 1
  • Larry S. Davis
    • 1
  1. 1.Department of Computer ScienceUniversity of MarylandCollege Park 

Personalised recommendations