Enhancing Image Classification with Class-Wise Clustered Vocabularies
Wojciech Wojcikiewicz, Alexander Binder and Motoaki Kawanabe
In: 2010 20th International Conference on Pattern Recognition (ICPR)(2010).

Abstract

In recent years bag-of-visual-words representations have gained increasing popularity in the field of image classification. Their performance highly relies on creating a good visual vocabulary from a set of image features (e.g. SIFT). For real-world photo archives such as Flicker, codebooks with larger than a few thousand words are desirable, which is infeasible by the standard k-means clustering. In this paper, we propose a two-step procedure which can generate more informative codebooks efficiently by class-wise k-means and a novel procedure for word selection. Our approach was compared favorably to the standard k-means procedure on the PASCAL VOC data sets.

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