Visual Object Categorization via Sparse Representation
Résumé
In this paper, we consider the problem of classifying a real
world image to the corresponding object class based on its visual
content via sparse representation, which is originally used as a
powerful tool for acquiring, representing and compressing high-dimensional signals.
Assuming the intuitive hypothesis that an image could be represented by a linear combination of the
training images from the same class, we propose a novel approach
for visual object categorization in which a sparse representation of
the image is first of all obtained by solving a l1 or l0-minimization
problem and then fed into a traditional classifier such as
Support Vector Machine (SVM) to finally perform the specified
task. Experimental results obtained on the SIMPLIcity database
have shown that this new approach can improve the
classification performance compared to standard SVM using directly features extracted from the image.