Delving into the representation learning of deep hashing

Searching for the nearest neighbor is a fundamental problem in the computer vision field, and deep hashing has become one of the most representative and widely used methods, which learns to generate compact binary codes for visual data. In this paper, we first delve into the representation learning of deep hashing and surprisingly find that deep hashing could be a double-edged sword, i.e., deep hashing can accelerate the query speed and decrease the storage cost in the nearest neighbor search progress, but it greatly sacrifices the discriminability of deep representations especially with extremely short target code lengths. To solve this problem, we propose a two-step deep hashing learning framework. The first step focuses on learning deep discriminative representations with metric learning. Subsequently, the learning framework concentrates on simultaneously learning compact binary codes and preserving representations learned in the former step from being sacrificed. Extensive experiments on two general image datasets and four challenging image datasets validate the effectiveness of our proposed learning framework. Moreover, the side effect of deep hashing is successfully mitigated with our learning framework. (C) 2022 The Author(s). Published by Elsevier B.V.

Automated summary

This paper investigates the representation learning problem of deep hashing in the nearest neighbor search. Experimental results demonstrate that although deep hashing can accelerate query speed and reduce storage cost, it sacrifices the discriminability of deep representations. To address this problem, a two-step deep hashing learning framework is proposed, which can simultaneously learn compact binary codes and protect deep representations from being sacrificed.