  Image retrieval based on deep convolutional features has demonstrated
state-of-the-art performance in popular benchmarks. In this paper, we present a
unified solution to address deep convolutional feature aggregation and image
re-ranking by simulating the dynamics of heat diffusion. A distinctive problem
in image retrieval is that repetitive or \emph{bursty} features tend to
dominate final image representations, resulting in representations less
distinguishable. We show that by considering each deep feature as a heat
source, our unsupervised aggregation method is able to avoid
over-representation of \emph{bursty} features. We additionally provide a
practical solution for the proposed aggregation method and further show the
efficiency of our method in experimental evaluation. Inspired by the
aforementioned deep feature aggregation method, we also propose a method to
re-rank a number of top ranked images for a given query image by considering
the query as the heat source. Finally, we extensively evaluate the proposed
approach with pre-trained and fine-tuned deep networks on common public
benchmarks and show superior performance compared to previous work.
