Bayesian denoising hashing for robust image retrieval. (February 2019)

• Record Type: Journal Article
• Authors: Wang, Dong; Song, Ge; Tan, Xiaoyang
• Is Part Of: Pattern recognition. Volume 86(2019:Feb.)
• Page Start: 134
• Page End: 142
• Publication Date: 2019-02
• Subjects: Image retrieval -- Denoising hashing -- Probabilistic model -- Variational Bayes; Pattern perception -- Periodicals; Perception des structures -- Périodiques; Patroonherkenning; 006.4
• DOI: 10.1016/j.patcog.2018.09.006
• Languages: English
• ISSNs: 0031-3203

Abstract

Highlights: A Bayesian denoising hashing method is proposed for robust image retrieval. We impose extra constraints in the variational Bayes framework to learn robust hash code. Our method can be combined with many traditional hashing algorithms to achieve state-of-the-art performance on real-world datasets. Abstract: Learning to hash is one of the most popular techniques in image retrieval, but few work investigates its robustness to noise corrupted images in which the unknown pattern of noise would heavily deteriorate the performance. To deal with this issue, we present in this paper a Bayesian denoising hashing algorithm whose output can be regarded a denoised version of the input hash code. We show that our method essentially seeks to reconstruct a new but more robust hash code by preserving the original input information while imposing extra constraints so as to correct the corrupted bits. We optimized this model in variational Bayes framework which has a closed-form update in each iteration that is more efficient than numerical optimization. Furthermore, our method can be added at the top of any original hashing layer, serving as a post-processing denoising layer with no change to previous training procedure. Experiments on three popular datasets demonstrate that the proposed method yields robust and meaningful hash code, which significantly improves the performance of state-of-the-art hash learning methods on challenging tasks such as large-scale natural image retrieval and retrieval with corrupted images.