Improving the Fisher Kernel for Large-Scale Image Classification - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2010

Improving the Fisher Kernel for Large-Scale Image Classification


The Fisher kernel (FK) is a generic framework which combines the benefits of generative and discriminative approaches. In the context of image classification the FK was shown to extend the popular bag-of-visual-words (BOV) by going beyond count statistics. However, in practice, this enriched representation has not yet shown its superiority over the BOV. In the first part we show that with several well-motivated modifications over the original framework we can boost the accuracy of the FK. On PASCAL VOC 2007 we increase the Average Precision (AP) from 47.9% to 58.3%. Similarly, we demonstrate state-of-the-art accuracy on CalTech 256. A major advantage is that these results are obtained using only SIFT descriptors and costless linear classifiers. Equipped with this representation, we can now explore image classification on a larger scale. In the second part, as an application, we compare two abundant resources of labeled images to learn classifiers: ImageNet and Flickr groups. In an evaluation involving hundreds of thousands of training images we show that classifiers learned on Flickr groups perform surprisingly well (although they were not intended for this purpose) and that they can complement classifiers learned on more carefully annotated datasets.
Fichier principal
Vignette du fichier
PSM10_0766.pdf (167.49 Ko) Télécharger le fichier
Vignette du fichier
poster.png (266.22 Ko) Télécharger le fichier
PSM10_ILSVRC.pdf (1.35 Mo) Télécharger le fichier
PSM10_poster.pdf (9.44 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Format : Figure, Image
Format : Other
Format : Other

Dates and versions

inria-00548630 , version 1 (20-12-2010)



Florent Perronnin, Jorge Sánchez, Thomas Mensink. Improving the Fisher Kernel for Large-Scale Image Classification. ECCV 2010 - European Conference on Computer Vision, Sep 2010, Heraklion, Greece. pp.143-156, ⟨10.1007/978-3-642-15561-1_11⟩. ⟨inria-00548630⟩
1845 View
5715 Download



Gmail Facebook X LinkedIn More