Hybrid Generative-Discriminative Object Recognition
Alex Holub, Max Welling, Pietro Perona
icon 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 [12], 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 unlabelled data. 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 [12], 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 unlabelled data.

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Text Reference

Alex Holub, Max Welling, and Pietro Perona. Hybrid generative-discriminative object recognition. Int. J. Computer Vision, 2007.

BibTeX Reference

@article{HolubWP_IJCV_2007,
    author = "Holub, Alex and Welling, Max and Perona, Pietro",
    title = "Hybrid Generative-Discriminative Object Recognition",
    journal = "Int. J. Computer Vision",
    year = "2007"
}