Large-scale image retrieval with compressed Fisher vectors
The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by...
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| Veröffentlicht in: | 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition S. 3384 - 3391 |
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| Hauptverfasser: | , , , |
| Format: | Tagungsbericht |
| Sprache: | Englisch |
| Veröffentlicht: |
IEEE
01.06.2010
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| Schlagworte: | |
| ISBN: | 1424469848, 9781424469840 |
| ISSN: | 1063-6919, 1063-6919 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | The problem of large-scale image search has been traditionally addressed with the bag-of-visual-words (BOV). In this article, we propose to use as an alternative the Fisher kernel framework. We first show why the Fisher representation is well-suited to the retrieval problem: it describes an image by what makes it different from other images. One drawback of the Fisher vector is that it is high-dimensional and, as opposed to the BOV, it is dense. The resulting memory and computational costs do not make Fisher vectors directly amenable to large-scale retrieval. Therefore, we compress Fisher vectors to reduce their memory footprint and speed-up the retrieval. We compare three binarization approaches: a simple approach devised for this representation and two standard compression techniques. We show on two publicly available datasets that compressed Fisher vectors perform very well using as little as a few hundreds of bits per image, and significantly better than a very recent compressed BOV approach. |
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| ISBN: | 1424469848 9781424469840 |
| ISSN: | 1063-6919 1063-6919 |
| DOI: | 10.1109/CVPR.2010.5540009 |