Fisher Vectors for Fine-Grained Visual Categorization

Jorge Sánchez 1 Florent Perronnin 2 Zeynep Akata 2, 3, *
* Corresponding author
3 LEAR - Learning and recognition in vision
Inria Grenoble - Rhône-Alpes, LJK - Laboratoire Jean Kuntzmann, INPG - Institut National Polytechnique de Grenoble
Abstract : The bag-of-visual-words (BOV) 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 excellent alternative to the BOV 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) BOV representations, we report significantly higher classification accuracies. For instance, on ImageNet10K we report 16.7% vs 6.4% top-1 accuracy (a 160% relative improvement).
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  • HAL Id : hal-00817681, version 1



Jorge Sánchez, Florent Perronnin, Zeynep Akata. Fisher Vectors for Fine-Grained Visual Categorization. FGVC Workshop in IEEE Computer Vision and Pattern Recognition (CVPR), IEEE, Jun 2011, Colorado Springs, United States. ⟨hal-00817681⟩



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