CASIA OpenIR  > 智能感知与计算研究中心
Towards Joint Multiply Semantics Hashing for Visual Search
Yunbo Wang1,2; Zhenan Sun1,2
2019
Conference NameThe 10th International Conference on Image and Graphics
Conference DateBeijing
Conference PlaceBeijing
Abstract

With the rapid growth of visual data on the web, deep hashing
has shown enormous potential in preserving semantic similarity for visual search. Currently, most of the existing hashing methods employ pairwise or triplet-wise constraint to obtain the semantic similarity or relatively similarity among binary codes. However, some potential semantic context cannot be fully exploited, resulting in a suboptimal visual search.
In this paper, we propose a novel deep hashing method, termed Joint Multiply Semantics Hashing (JMSH), to learn discriminative yet compact binary codes.
In our approach, We jointly learn multiply semantic information to perform feature learning and
hash coding.
To be specific, the semantic information includes the pairwise semantic similarity between binary codes, the pointwise binary codes' semantics and the pointwise visual feature' semantics.
Meanwhile, three different loss functions are designed to train the JMSH model. Extensive experiments show that the proposed JMSH yields state-of-the-art retrieval performance on representative image retrieval benchmarks.

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28386
Collection智能感知与计算研究中心
Corresponding AuthorYunbo Wang
Affiliation1.Institute of Automation, Chinese Academy of Sciences
2.University of Chinese Academy of Sciences, Beijing
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Yunbo Wang,Zhenan Sun. Towards Joint Multiply Semantics Hashing for Visual Search[C],2019.
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