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Hierarchical Recurrent Neural Hashing for Image Retrieval with Hierarchical Convolutional Features
Lu, Xiaoqiang; Chen, Yaxiong; Li, Xuelong
Department光学影像学习与分析中心
2018-01
Source PublicationIEEE Transactions on Image Processing
ISSN10577149;
Volume27Issue:1Pages:106-120
Contribution Rank1
AbstractHashing has been an important and effective technology in image retrieval due to its computational efficiency and fast search speed. The traditional hashing methods usually learn hash functions to obtain binary codes by exploiting hand-crafted features, which cannot optimally represent the information of the sample. Recently, deep learning methods can achieve better performance, since deep learning architectures can learn more effective image representation features. However, these methods only use semantic features to generate hash codes by shallow projection but ignore texture details. In this paper, we proposed a novel hashing method, namely hierarchical recurrent neural hashing (HRNH), to exploit hierarchical recurrent neural network to generate effective hash codes. There are three contributions of this paper. First, a deep hashing method is proposed to extensively exploit both spatial details and semantic information, in which, we leverage hierarchical convolutional features to construct image pyramid representation. Second, our proposed deep network can exploit directly convolutional feature maps as input to preserve the spatial structure of convolutional feature maps. Finally, we propose a new loss function that considers the quantization error of binarizing the continuous embeddings into the discrete binary codes, and simultaneously maintains the semantic similarity and balanceable property of hash codes. Experimental results on four widely used data sets demonstrate that the proposed HRNH can achieve superior performance over other state-of-the-art hashing methods. © 1992-2012 IEEE.
DOI10.1109/TIP.2017.2755766
Indexed ByEI
Language英语
PublisherInstitute of Electrical and Electronics Engineers Inc.
EI Accession Number20174104260201
Citation statistics
Document Type期刊论文
Identifierhttp://ir.opt.ac.cn/handle/181661/30845
Collection光学影像学习与分析中心
AffiliationXi'An Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an, China
Recommended Citation
GB/T 7714
Lu, Xiaoqiang,Chen, Yaxiong,Li, Xuelong. Hierarchical Recurrent Neural Hashing for Image Retrieval with Hierarchical Convolutional Features[J]. IEEE Transactions on Image Processing,2018,27(1):106-120.
APA Lu, Xiaoqiang,Chen, Yaxiong,&Li, Xuelong.(2018).Hierarchical Recurrent Neural Hashing for Image Retrieval with Hierarchical Convolutional Features.IEEE Transactions on Image Processing,27(1),106-120.
MLA Lu, Xiaoqiang,et al."Hierarchical Recurrent Neural Hashing for Image Retrieval with Hierarchical Convolutional Features".IEEE Transactions on Image Processing 27.1(2018):106-120.
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