Fisher Kernels on Visual Vocabularies for Image Categorization
Within the field of pattern classification, the Fisher kernel is a powerful framework which combines the strengths of generative and discriminative approaches. The idea is to characterize a signal with a gradient vector derived from a generative probability model and to subsequently feed this repres...
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| Vydáno v: | 2007 IEEE Conference on Computer Vision and Pattern Recognition s. 1 - 8 |
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| Hlavní autoři: | , |
| Médium: | Konferenční příspěvek |
| Jazyk: | angličtina japonština |
| Vydáno: |
IEEE
01.06.2007
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| Témata: | |
| ISBN: | 9781424411795, 1424411793 |
| ISSN: | 1063-6919, 1063-6919 |
| On-line přístup: | Získat plný text |
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| Shrnutí: | Within the field of pattern classification, the Fisher kernel is a powerful framework which combines the strengths of generative and discriminative approaches. The idea is to characterize a signal with a gradient vector derived from a generative probability model and to subsequently feed this representation to a discriminative classifier. We propose to apply this framework to image categorization where the input signals are images and where the underlying generative model is a visual vocabulary: a Gaussian mixture model which approximates the distribution of low-level features in images. We show that Fisher kernels can actually be understood as an extension of the popular bag-of-visterms. Our approach demonstrates excellent performance on two challenging databases: an in-house database of 19 object/scene categories and the recently released VOC 2006 database. It is also very practical: it has low computational needs both at training and test time and vocabularies trained on one set of categories can be applied to another set without any significant loss in performance. |
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| ISBN: | 9781424411795 1424411793 |
| ISSN: | 1063-6919 1063-6919 |
| DOI: | 10.1109/CVPR.2007.383266 |