Generalized Debiased Semi-Supervised Hashing for Large-Scale Image Retrieval

Abstract

Semi-supervised hashing has shown promising efficacy in large-scale image retrieval, which learns similarity-preserving codes from both labeled and unlabeled data. To enable the use of advanced supervised hashing techniques, pseudo labels are widely applied. However, existing methods typically suffer from a biased learning issue due to pseudo label noise, which can be further aggravated during optimization. Although such a bias can adversely affect hashing accuracy, it has not been investigated sufficiently. In view of this, we present a comprehensive discussion on potential causes of biases, involving processes of pseudo-labeling, hash learning and optimization. Accordingly, a novel Generalized Debiased Semi-supervised Hashing (GDSH) method is proposed as a unified solution to mitigate the biases. Specifically, reliable pseudo labels are first predicted via a robust label completion strategy. Secondly, a debiased hash learning module is designed by combining label denoising and similarity updating. This can not only refine the supervision, but also obtain hash codes that are semantically debiased in both category and sample levels. Finally, a discrete semi-supervised hashing algorithm is proposed to alleviate the bias arising from optimization. Experimental results on three single-label and three multi-label image benchmarks demonstrate that GDSH remarkably outperforms the state-of-the-arts in different semi-supervised settings.