INVESTIGADORES
SANCHEZ Jorge Adrian
congresos y reuniones científicas
Título:
Fisher vectors for fine-grained visual categorization
Autor/es:
JORGE SÁNCHEZ; FLORENT PERRONNIN; ZEYNEP AKATA
Lugar:
Colorado-Springs
Reunión:
Workshop; First Workshop on Fine-Grained Visual Categorization; 2011
Institución organizadora:
IEEE, CVPR
Resumen:
The bag-of-visual-words (BOW) is certainly the most popular image representation to date and it has been shown to yield good results in various problems including Fine-Grained Visual Categorization (FGVC) [3, 4]. Our contribution is to show that the Fisher Vector (FV) which describes an image by its deviation from an average model is an alternative which performs much better than the BOW for the FGVC problem. In this extended abstract we first provide a brief introduction to the FV. We then present theoretical as well as practical motivations for using the FV for FGVC. We finally provide experimental results on four ImageNet subsets: fungus, ungulate, vehicle and ImageNet10K. Compared to [4] which uses spatial pyramid (SP) BOW representations, we report significantly higher classification accuracies. For instance, on ImageNet10K we report 16.7% vs 6.4% top-1 accuracy which represents a 160% relative improvement.
