Class Specific Object Recognition using Kernel Gibbs Distributions

Feature selection is crucial for effective object recognition. The subject has been vastly investigated in
the literature, with approaches spanning from heuristic choices to statistical methods, to integration of multiple
cues. For all these techniques the final result is a common feature representation for all the considered
object classes. In this paper we take a completely different approach, using class specific features. Our
method consists of a probabilistic classifier that allows us to use separate feature vectors, selected specifically
for each class. We obtain this result by extending previous work on Class Specific Classifiers and
Kernel Gibbs distributions. The resulting method, that we call Kernel-Class Specific Classifier, allows us
to use a different kernel for each object class by learning it. We present experiments of increasing level of
difficulty, showing the power of our approach.