Event Location: NSH 1507

Abstract: A popular approach to problems in image classification is to represent the
image as a bag of visual words and then employ a classifier to categorize
the image. Unfortunately, a significant shortcoming of this approach is
that the clustering and classification are disconnected. Since the
clustering into visual words is unsupervised, the representation does not necessarily
capture the aspects of the data that are most useful for classification.
More seriously, the semantic relationship between clusters is lost,
causing the overall classification performance to suffer.
We introduce “discriminative cluster refinement” (DCR), a method that
explicitly models the pairwise relationships between different visual
words by exploiting their co-occurrence information. The assigned class
labels are used to identify the co-occurrence patterns that are most informative
for object classification. DCR employs a maximum-margin approach to
generate an optimal kernel matrix for classification. One important
benefit of DCR is that it integrates smoothly into existing bag-of-words information
retrieval systems by employing the set of visual words generated by any
clustering method. While DCR could improve a broad class of information
retrieval systems, this paper focuses on object category recognition. We
present a direct comparison with a state-of-the art method on the PASCAL
2006 database and show that cluster refinement results in a significant
improvement in classification accuracy given a small number of training
examples.