Combining Generative Models and Fisher Kernels for Object Recognition
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 [1] which retains most of
the desirable properties of generative methods, while increasing the
classification performance through a discriminative setting. Furthermore, we
demonstrate how this kernel framework can be used to combine different types
of features and models into a single classifier. Our experiments, conducted
on a number of popular benchmarks, show strong performance improvements over
the corresponding generative approach and are competitive with the best results
reported in the literature.
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Text Reference
Alex D. Holub, Max Welling, and Pietro Perona. Combining generative models and fisher kernels for object recognition. In ICCV, I: 136–143. 2005.BibTeX Reference
@inproceedings{HolubWP_ICCV_2005,AUTHOR = "Holub, Alex D. and Welling, Max and Perona, Pietro",
TAG = "object_recognition",
TITLE = "Combining Generative Models and Fisher Kernels for Object Recognition",
BOOKTITLE = "ICCV",
YEAR = "2005",
PAGES = "I: 136-143",
BIBSOURCE = "http://www.visionbib.com/bibliography/applicat812.html#TT61973"
}