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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
(unavailable)
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.
@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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