Progressive Generative Hashing for Image Retrieval

By Yuqing Ma, Yue He, Fan Ding, Sheng Hu, Jun Yu Li, Xianglong Liu. 2018

21 citations recorded by the source corpus.

Recent years have witnessed the success of the emerging hashing techniques in large-scale image retrieval. Owing to the great learning capacity, deep hashing has become one of the most promising solutions, and achieved attractive performance in practice. However, without semantic label information, the unsupervised deep hashing still remains an open question. In this paper, we propose a novel progressive generative hashing (PGH) framework to help learn a discriminative hashing network in an unsupervised way. Very different from existing studies, it first treats the hash codes as a kind of semantic condition for the similar image generation, and simultaneously feeds the original image and its codes into the generative adversarial networks (GANs). The real images together with the synthetic ones can further help train a discriminative hashing network based on a triplet loss. By iteratively inputting the learnt codes into the hash conditioned GANs, we can progressively enable the hashing network to discover the semantic relations. Extensive experiments on the widely-used image datasets demonstrate that PGH can significantly outperforms state-of-the-art unsupervised hashing methods.

Subfields: Computer Vision and Pattern Recognition

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In Computer Vision and Pattern Recognition

1. Xianglong Liu
2. Dacheng Tao
3. Peng Cui
4. Shihao Bai
5. Aishan Liu
6. Yue He
7. Edwin R. Hancock
8. Jiaxin Fan

Citations in the corpus

• Regionwise Generative Adversarial Image Inpainting for Large Missing Areas (2019, background): “Notable successes include image classification [27]–[29], image generation [30]–[32], representation learning [33], [34], image retrieval [35], [36], object detection [37]–[39], video applications [40], and image translation [41].”
• Perceptual-Sensitive GAN for Generating Adversarial Patches (2019, background): “Note that our our PS-GAN differs widely from the conditional GAN (cGAN) (Mirza and Osindero 2014), even we can also treat the input patch as the conditions.”
• Towards Non-I.I.D. image classification: A dataset and baselines (2019, background): “ Tsinghua University arXiv:1906.02899v3 [cs.CV] 14 Aug 2019 1. Introduction In recent years, machine learning has achieved remarkable progress, mainly owing to the development of deep neural networks [1, 2, 3, 4, 5, 6]. One basic hypothesis of machine learning models is that the training and testing”