Alan K. Mackworth's Publications

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Combining Automated Visual Search and Place Categorization

Pooja Viswanathan, D. Meger, T. Southey, S. Helmer, S. McCann, M. Muja, M Dockrey, M. Joya, D. G. Lowe, J. J. Little, and Alan K. Mackworth. Combining Automated Visual Search and Place Categorization. In Proceedings of the CVPR Workshop on Visual Place Categorization, 2009. Invited Paper

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Abstract

Places in an environment can be described by the objects they contain. This paper discusses the completely automated integration of object detection and place classification in a single system. We first perform automated learning of object-place relations from an online annotated database. We then train object detectors on some of the most frequently occurring objects. Finally we use detection scores as well as learned object-place relations to perform place classification of images. We also discuss areas for improvement and the application of this work to informed visual search. As a whole, the system demonstrates the automated acquisition of training data containing labeled instances (i.e. bounding boxes) and the performance of a state-of-the-art object detection technique trained on this data to perform place classification of realistic indoor scenes.

BibTeX

@InProceedings{PoojaCVPR09,
  author =	 {Pooja Viswanathan and D. Meger and T. Southey and S. Helmer and S. McCann and M. Muja and M Dockrey and M. Joya and D. G. Lowe and J. J. Little and Alan K. Mackworth},
  title =	 {Combining Automated Visual Search and Place Categorization},
  year =	 {2009}, 
  booktitle =	{Proceedings of the CVPR Workshop on Visual Place Categorization}, 
  note =         {{Invited Paper}},
  abstract =	 {Places in an environment can be described by the objects
they contain. This paper discusses the completely
automated integration of object detection and place classification
in a single system. We first perform automated
learning of object-place relations from an online annotated
database. We then train object detectors on some of the
most frequently occurring objects. Finally we use detection
scores as well as learned object-place relations to perform
place classification of images. We also discuss areas
for improvement and the application of this work to informed
visual search. As a whole, the system demonstrates
the automated acquisition of training data containing labeled
instances (i.e. bounding boxes) and the performance
of a state-of-the-art object detection technique trained on
this data to perform place classification of realistic indoor
scenes.
},
  bib2html_pubtype ={Refereed Conference Proceeding},
  bib2html_rescat ={},
}

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