Visual categorization is fundamentally important for autonomous
mobile robots to get intelligence such as novel
object acquisition and topological place recognition. The
main difficulty of visual categorization is how to reduce the
large intra-class variations. In this paper, we present a new
method made robust to that problem by using intermediate
blurring and entropy-guided codebook selection in a bagof-
words framework. Intermediate blurring can reduce the
high frequency of surface markings and provide dominant
shape information. Entropy of a hypothesized codebook can
provide the necessary amount of repetition among training
exemplars. A generative optimal codebook for each
category is learned using the MDL (minimum description
length) principle guided by entropy information. Finally, a
discriminative codebook is learned using the discriminative
method guided by the inter-category entropy of the codebook.
We validate the effect of the proposed method using a
Caltech-101 DB, which has large intra-class variations.