Revisiting the Fisher vector for fine-grained classification

• Wining method of Fine-grain image classification challenge 2013.
• Late combination of two indexing and classification strategies.
• Good practices for fine grain image classification.
• Key features: descriptors filtering, spatial coordinates coding, active learning.

This paper describes the joint submission of Inria and Xerox to their joint participation to the FGCOMP’2013 challenge. Although the proposed system follows most of the standard Fisher classification pipeline, we describe a few key features and good practices that significantly improve the accuracy when specifically considering fine-grain classification tasks. In particular, we consider the late fusion of two systems both based on Fisher vectors, but for which we choose drastically design choices that make them very complementary. Moreover, we propose a simple yet effective filtering strategy, which significantly boosts the performance for several class domains.