Fisher Kernel for Deep Neural Activations
Abstract
Compared to image representation based on low-level local descriptors, deep neural activations of Convolutional Neural Networks (CNNs) are richer in mid-level representation, but poorer in geometric invariance properties. In this paper, we present a straightforward framework for better image representation by combining the two approaches. To take advantages of both representations, we propose an efficient method to extract a fair amount of multi-scale dense local activations from a pre-trained CNN. We then aggregate the activations by Fisher kernel framework, which has been modified with a simple scale-wise normalization essential to make it suitable for CNN activations. Replacing the direct use of a single activation vector with our representation demonstrates significant performance improvements: +17.76 (Acc.) on MIT Indoor 67 and +7.18 (mAP) on PASCAL VOC 2007. The results suggest that our proposal can be used as a primary image representation for better performances in visual recognition tasks.
Cite
@article{arxiv.1412.1628,
title = {Fisher Kernel for Deep Neural Activations},
author = {Donggeun Yoo and Sunggyun Park and Joon-Young Lee and In So Kweon},
journal= {arXiv preprint arXiv:1412.1628},
year = {2015}
}