  This paper provides an extensive study on the availability of image
representations based on convolutional networks (ConvNets) for the task of
visual instance retrieval. Besides the choice of convolutional layers, we
present an efficient pipeline exploiting multi-scale schemes to extract local
features, in particular, by taking geometric invariance into explicit account,
i.e. positions, scales and spatial consistency. In our experiments using five
standard image retrieval datasets, we demonstrate that generic ConvNet image
representations can outperform other state-of-the-art methods if they are
extracted appropriately.
