PAL lab meeting 21 June, 2006 (Vincent): Multiclass Object Recognition with Sparse, Localized Features.

Author : Jim Mutch and David G. Lowe @ cs.ubc.ca
This paper appears in CVPR'06.

Abstract :

We apply a biologically inspired model of visual object
recognition to the multiclass object categorization problem.
Our model modifies that of Serre, Wolf, and Poggio. As in
that work, we first apply Gabor filters at all positions and
scales; feature complexity and position/scale invariance are
then built up by alternating template matching and max
pooling operations. We refine the approach in several biologically
plausible ways, using simple versions of sparsification
and lateral inhibition. We demonstrate the value of
retaining some position and scale information above the intermediate
feature level. Using feature selection we arrive
at a model that performs better with fewer features. Our
final model is tested on the Caltech 101 object categories
and the UIUC car localization task, in both cases achieving
state-of-the-art performance. The results strengthen the
case for using this class of model in computer vision.

Here you can find the file.