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arXiv · 1805.08587

Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval

Shanmin Pang·Jin Ma·Jianru Xue·Jihua Zhu·Vicente Ordonez
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Abstract

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.

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Shanmin Pang, Jin Ma, Jianru Xue, Jihua Zhu, Vicente Ordonez. 2018-05-22. Deep Feature Aggregation and Image Re-ranking with Heat Diffusion for Image Retrieval. https://arxiv.org/abs/1805.08587

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