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Shrinking large visual vocabularies using multi-label agglomerative information bottleneck

: Wojcikiewicz, W.; Binder, A.; Kawanabe, M.


Institute of Electrical and Electronics Engineers -IEEE-; IEEE Signal Processing Society:
17th IEEE International Conference on Image Processing, ICIP 2010 : Hong Kong, 26 - 29. September 2010
Piscataway/NJ: IEEE, 2010
ISBN: 978-1-4244-7994-8 (online)
ISBN: 978-1-4244-7992-4 (print)
ISBN: 978-1-4244-7993-1
ISSN: 1522-4880
International Conference on Image Processing (ICIP) <17, 2010, Hong Kong>
Conference Paper
Fraunhofer FIRST ()

The quality of visual vocabularies is crucial for the performance of bag-of-words image classification methods. Several approaches have been developed for codebook construction, the most popular method is to cluster a set of image features (e.g. SIFT) by k-means. In this paper, we propose a two-step procedure which incorporates label information into the clustering process by efficiently generating a large and informative vocabulary using class-wise k-means and reducing its size by agglomerative information bottleneck (AIB). We introduce an extension of the AIB procedure for multi-label problems and show that this two-step approach improves the classification results while reducing computation time compared to the vanilla k-means. We analyse the reasons for the performance gain on the PASCAL VOC 2007 data set.