Title: Object and Action Classification with Latent Window Parameters
Authors: Bilen, Hakan ×
Namboodiri, Vinay
Van Gool, Luc #
Issue Date: Feb-2014
Publisher: Kluwer Academic Publishers
Series Title: International Journal of Computer Vision vol:106 issue:3 pages:237-251
Abstract: In this paper we propose a generic frame-
work to incorporate unobserved auxiliary information
for classifying objects and actions. This framework al-
lows us to automatically select a bounding box and its
quadrants from which best to extract features. These
spatial subdivisions are learnt as latent variables. The
paper is an extended version of our earlier work [2],
complemented with additional ideas, experiments and
We approach the classification problem in a discrim-
inative setting, as learning a max-margin classifier that
infers the class label along with the latent variables.
Through this paper we make the following contribu-
tions: a) we provide a method for incorporating latent
variables into object and action classification; b) these
variables determine the relative focus on foreground vs.
background information that is taken account of; c) we
design an objective function to more effectively learn in
unbalanced data sets; d) we learn a better classifier by
iterative expansion of the latent parameter space. We
demonstrate the performance of our approach through experimental evaluation on a number of standard object and action recognition data sets.
Description: Bilen H., Namboodiri V.P., Van Gool L., ''Object and action classification with latent window parameters'', International journal of computer vision, vol. 106, no. 3, pp. 237-251, February 2014.
ISSN: 0920-5691
Publication status: published
KU Leuven publication type: IT
Appears in Collections:ESAT - PSI, Processing Speech and Images
× corresponding author
# (joint) last author

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