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Learning and using taxonomies for fast visual categorization

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2 Author(s)
Gregory Griffin ; California Institute of Technology, Computation and Neural Systems Dept., Pasadena, 91125, USA ; Pietro Perona

The computational complexity of current visual categorization algorithms scales linearly at best with the number of categories. The goal of classifying simultaneously Ncat = 104 - 105 visual categories requires sub-linear classification costs. We explore algorithms for automatically building classification trees which have, in principle, logNcat complexity. We find that a greedy algorithm that recursively splits the set of categories into the two minimally confused subsets achieves 5-20 fold speedups at a small cost in classification performance. Our approach is independent of the specific classification algorithm used. A welcome by-product of our algorithm is a very reasonable taxonomy of the Caltech-256 dataset.

Published in:

Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on

Date of Conference:

23-28 June 2008