INVESTIGADORES
SANCHEZ Jorge Adrian
congresos y reuniones científicas
Título:
Improving the Fisher Kernel for Large-Scale Image Classification
Autor/es:
FLORENT PERRONNIN; JORGE SÁNCHEZ; THOMAS MENSINK
Reunión:
Conferencia; 11th European conference on Computer vision; 2010
Resumen:
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.