Fisher vectors (FV) have shown great advantages in large scale visual search. However, traditional FV suffers from noisy local descriptors, which may deteriorate the FV discriminative power. In this paper, we propose a robust Fisher vectors (RFV). To fulfill fast search and light storage over a large scale image dataset, we employ a simple binarization method to compress RFV to generate compact robust Fisher codes (RFC). Extensive comparison experiments on benchmark datasets have shown that both RFV and RFC outperforms the state-of-the-art performance. The scalability of RFC has been validated on a dataset of over 1 million images as well.
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