Categorizing visual elements is fundamentally important
for autonomous mobile robots to get intelligence such
as new object acquisition and topological place classification.
The main problem of visual categorization is how to reduce
the large intra-class variations, especially surface markings
of man-made objects. In this paper, we present a robust
method by introducing intermediate blurring and entropyguided
codebook selection in a bag-of-words framework. Intermediate
blurring can filter out the high frequency of surface
markings and provide dominant shape information. Entropy
of a hypothesized codebook can provide the necessary measure
for the semantic parts among training exemplars. From the
first step, a generative optimal codebook for each category is
learned using the MDL (minimum description length) principle
guided by entropy information. From the second step, a final set
of codebook is learned using the discriminative method guided
by the inter-category entropy of the codebook. We select the
necessary parameters through various evaluations and validate
the effect of the surface marking reduction method using a
Caltech-101 DB, which has large intra-class variations. Finally,
we briefly introduce the impact of the method to the object
categorization and segmentation problem.