Improving the Fisher Kernel for Large-Scale Image Classification View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2010

AUTHORS

Florent Perronnin , Jorge Sánchez , Thomas Mensink

ABSTRACT

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. More... »

PAGES

143-156

References to SciGraph publications

  • 2004-11. Distinctive Image Features from Scale-Invariant Keypoints in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007-06. Local Features and Kernels for Classification of Texture and Object Categories: A Comprehensive Study in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2011-03. Pegasos: primal estimated sub-gradient solver for SVM in MATHEMATICAL PROGRAMMING
  • Book

    TITLE

    Computer Vision – ECCV 2010

    ISBN

    978-3-642-15560-4
    978-3-642-15561-1

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-642-15561-1_11

    DOI

    http://dx.doi.org/10.1007/978-3-642-15561-1_11

    DIMENSIONS

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