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arXiv · 1501.04711

DeepHash: Getting Regularization, Depth and Fine-Tuning Right

Jie Lin·Olivier Morere·Vijay Chandrasekhar·Antoine Veillard·Hanlin Goh
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Abstract

This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval. We propose DeepHash: a hashing scheme based on deep networks. Key to making DeepHash work at extremely low bitrates are three important considerations -- regularization, depth and fine-tuning -- each requiring solutions specific to the hashing problem. In-depth evaluation shows that our scheme consistently outperforms state-of-the-art methods across all data sets for both Fisher Vectors and Deep Convolutional Neural Network features, by up to 20 percent over other schemes. The retrieval performance with 256-bit hashes is close to that of the uncompressed floating point features -- a remarkable 512 times compression.

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Jie Lin, Olivier Morere, Vijay Chandrasekhar, Antoine Veillard, Hanlin Goh. 2015-01-20. DeepHash: Getting Regularization, Depth and Fine-Tuning Right. https://arxiv.org/abs/1501.04711

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