The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Large-scale image retrieval with compressed Fisher vectors
Abstract
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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Cites
Video Google: a text retrieval approach to object matching in videos
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Hamming Embedding and Weak Geometric Consistency for Large Scale Image Search
Jegou, H., Douze, M., & Schmid, C. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_24 · Cited by 1,901
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Torralba, A., Fergus, R., & Weiss, Y. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587633 · Cited by 727
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Self-citationImage Classification with the Fisher Vector: Theory and Practice
Sánchez, J., Perronnin, F., Mensink, T., & Verbeek, J. (2013) · International Journal of Computer Vision · doi:10.1007/s11263-013-0636-x · Cited by 1,498
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Image retrieval using scene graphs
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Partial Copy Detection in Videos: A Benchmark and an Evaluation of Popular Methods
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Compact Video Description for Copy Detection with Precise Temporal Alignment
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Metadata
- Authors
- Perronnin, F., Liu, Y., Sanchez, J., & Poirier, H.
- Venue
- 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2010-06
- Pages
- 3384-3391
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2071027807
- Cited by
- 761
- References
- 3
- FWCI
- 24.74
- Type
- conference paper
- Language
- en
- Open access
- closed
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