基于伪成对标签的深度无监督哈希学习

doi:10.16451/j.cnki.issn1003-6059.202003007

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Abstract It is difficult to obtain high-quality hash codes for unsupervised deep hashing methods due to the lack of similarity supervised information. Therefore, an end-to-end deep unsupervised hashing model based on pseudo-pairwise labels is proposed. Statistical analysis is performed on the image features extracted by the pre-trained deep convolutional neural network to construct the semantic similarity labels for data. Supervised deep hashing based on pairwise labels is then conducted. Experiments on commonly used image datasets CIFAR-10 and NUS-WIDE indicate that hash codes obtained by the proposed method perform better on image retrieval.

Key words： Learning to Hash Deep Unsupervised Hashing Pseudo Label Approximate Nearest Neighbor Search Image Retrieval

Received: 12 November 2019

ZTFLH:

TP 391

Fund:Supported by National Natural Science Foundation of China(No. 61572443), Natural Science Foundation of Zhejiang Province (No. LY14F020019)

Corresponding Authors: LIU Huawen, Ph.D., professor. His research interests include data mining, feature selection and machine learning.

About author:: LIN Jiwen, master student. His research interests include learning to hash and large-scale image retrieval.

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Cite this article:

LIN Jiwen,LIU Huawen. Deep Unsupervised Hashing with Pseudo Pairwise Labels[J]. , 2020, 33(3): 258-267.

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http://manu46.magtech.com.cn/Jweb_prai/EN/10.16451/j.cnki.issn1003-6059.202003007 OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2020/V33/I3/258

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