Hybrid Generative-Discriminative Visual Categorization |
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Authors: | Alex D Holub Max Welling Pietro Perona |
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Affiliation: | (1) Computation and Neural Systems, California Institute of Technology, MC 136-93, Pasadena, CA 91125, USA;(2) Department of Computer Science, University of California Irvine, Irvine, CA 92697-3425, USA |
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Abstract: | Learning models for detecting and classifying object categories is a challenging problem in machine vision. While discriminative
approaches to learning and classification have, in principle, superior performance, generative approaches provide many useful
features, one of which is the ability to naturally establish explicit correspondence between model components and scene features—this,
in turn, allows for the handling of missing data and unsupervised learning in clutter. We explore a hybrid generative/discriminative
approach, using ‘Fisher Kernels’ (Jaakola, T., et al. in Advances in neural information processing systems, Vol. 11, pp. 487–493,
1999), which retains most of the desirable properties of generative methods, while increasing the classification performance through
a discriminative setting. Our experiments, conducted on a number of popular benchmarks, show strong performance improvements
over the corresponding generative approach. In addition, we demonstrate how this hybrid learning paradigm can be extended
to address several outstanding challenges within computer vision including how to combine multiple object models and learning
with unlabeled data. |
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Keywords: | Machine learning Object recognition Discriminative learning Support vector machines |
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