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Local Semantic-Aware Deep Hashing With Hamming-Isometric Quantization
Wang, Yunbo1,2; Liang, Jian1,2; Cao, Dong1; Sun, Zhenan2,3
Source PublicationIEEE TRANSACTIONS ON IMAGE PROCESSING
ISSN1057-7149
2019-06-01
Volume28Issue:6Pages:2665-2679
Abstract

Hashing is a promising approach for compact storage and efficient retrieval of big data. Compared to the conventional hashing methods using handcrafted features, emerging deep hashing approaches employ deep neural networks to learn both feature representations and hash functions, which have been proven to be more powerful and robust in real-world applications. Currently, most of the existing deep hashing methods construct pairwise or triplet-wise constraints to obtain similar binary codes between a pair of similar data points or relatively similar binary codes within a triplet. However, we argue that some critical local structures have not been fully exploited. So, this paper proposes a novel deep hashing method named local semantic-aware deep hashing with Hamming-isometric quantization (LSDH), aiming to make full use of local similarity in hash function learning. Specifically, the potential semantic relation is exploited to robustly preserve local similarity of data in the Hamming space. In addition to reducing the error introduced by binary quantizing, a Hamming-isometric objective is designed to maximize the consistency of similarity between the pairwise binary-like features and corresponding binary codes pair, which is shown to be able to improve the quality of binary codes. Extensive experimental results on several benchmark datasets, including three singlelabel datasets and one multi-label dataset, demonstrate that the proposed LSDH achieves better performance than the latest state-of-the-art hashing methods.

KeywordImage retrieval deep hashing similarity-preserving local structures Hamming-isometric
DOI10.1109/TIP.2018.2889269
WOS KeywordIMAGE RETRIEVAL
Indexed BySCI
Language英语
Funding ProjectNational Key Research and Development Program of China[2016YFB1001000] ; National Natural Science Foundation of China[U1836217] ; National Natural Science Foundation of China[61427811] ; National Natural Science Foundation of China[61573360] ; National Key Research and Development Program of China[2017YFC0821602]
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000462386000003
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/23481
Collection中国科学院自动化研究所
Corresponding AuthorSun, Zhenan
Affiliation1.Chinese Acad Sci, Inst Automat, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, Ctr Excellence Brain Sci & Intelligence Technol, Ctr Res Intelligent Percept & Comp, Beijing 100190, Peoples R China
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang, Yunbo,Liang, Jian,Cao, Dong,et al. Local Semantic-Aware Deep Hashing With Hamming-Isometric Quantization[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2019,28(6):2665-2679.
APA Wang, Yunbo,Liang, Jian,Cao, Dong,&Sun, Zhenan.(2019).Local Semantic-Aware Deep Hashing With Hamming-Isometric Quantization.IEEE TRANSACTIONS ON IMAGE PROCESSING,28(6),2665-2679.
MLA Wang, Yunbo,et al."Local Semantic-Aware Deep Hashing With Hamming-Isometric Quantization".IEEE TRANSACTIONS ON IMAGE PROCESSING 28.6(2019):2665-2679.
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