Cascaded Deep Hashing for Large-Scale Image Retrieval

2018 ◽  
pp. 419-429
Author(s):  
Jun Lu ◽  
Li Zhang
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval

An Enhanced Deep Hashing Method for Large-Scale Image Retrieval

2019 ◽  
pp. 382-393
Author(s):  
Cong Chen ◽  
Weiqin Tong ◽  
Xuehai Ding ◽  
Xiaoli Zhi
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval

scholarly journals Spatial pyramid deep hashing for large-scale image retrieval

2017 ◽  
Vol 243 ◽  
pp. 166-173 ◽  
Author(s):  
Wanqing Zhao ◽  
Hangzai Luo ◽  
Jinye Peng ◽  
Jianping Fan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval ◽  
Spatial Pyramid

Deep hashing for large-scale image retrieval

2017 ◽  
Author(s):  
Li Mengting ◽  
Liu Jun
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval

Hierarchical Deep Hashing for Fast Large Scale Image Retrieval

2021 ◽  
Author(s):  
Yongfei Zhang ◽  
Cheng Peng ◽  
Jingtao Zhang ◽  
Xianglong Liu ◽  
Shiliang Pu ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval

scholarly journals Deep Hashing Based Fusing Index Method for Large-Scale Image Retrieval

2017 ◽  
Vol 2017 ◽  
pp. 1-8
Author(s):  
Lijuan Duan ◽  
Chongyang Zhao ◽  
Jun Miao ◽  
Yuanhua Qiao ◽  
Xing Su
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Nearest Neighbor ◽  
Feature Learning ◽  
Index Method ◽  
Storage And Retrieval ◽  
Deep Hashing ◽  
Indexing Method ◽  
Hash Code ◽  
Large Scale Image Retrieval

Hashing has been widely deployed to perform the Approximate Nearest Neighbor (ANN) search for the large-scale image retrieval to solve the problem of storage and retrieval efficiency. Recently, deep hashing methods have been proposed to perform the simultaneous feature learning and the hash code learning with deep neural networks. Even though deep hashing has shown the better performance than traditional hashing methods with handcrafted features, the learned compact hash code from one deep hashing network may not provide the full representation of an image. In this paper, we propose a novel hashing indexing method, called the Deep Hashing based Fusing Index (DHFI), to generate a more compact hash code which has stronger expression ability and distinction capability. In our method, we train two different architecture’s deep hashing subnetworks and fuse the hash codes generated by the two subnetworks together to unify images. Experiments on two real datasets show that our method can outperform state-of-the-art image retrieval applications.

scholarly journals IPDH: An Improved Pairwise-Based Deep Hashing Method for Large-Scale Image Retrieval

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 167504-167515
Author(s):  
Wei Yao ◽  
Feifei Lee ◽  
Lu Chen ◽  
Chaowei Lin ◽  
Shuai Yang ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval

scholarly journals Progressive Generative Hashing for Image Retrieval

2018 ◽  
Author(s):  
Yuqing Ma ◽  
Yue He ◽  
Fan Ding ◽  
Sheng Hu ◽  
Jun Li ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Generative Adversarial Networks ◽  
Learning Capacity ◽  
Deep Hashing ◽  
Adversarial Networks ◽  
Label Information ◽  
Semantic Label ◽  
Large Scale Image Retrieval ◽  
Open Question

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.

scholarly journals Semantic Hierarchy Preserving Deep Hashing for Large-Scale Image Retrieval

2021 ◽  
Author(s):  
Ming Zhang ◽  
Xuefei Zhe ◽  
Le Ou-Yang ◽  
Shifeng Chen ◽  
Hong Yan
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Semantic Hierarchy ◽  
Large Scale Image Retrieval

Multiple hierarchical deep hashing for large scale image retrieval

2017 ◽  
Vol 77 (9) ◽  
pp. 10471-10484 ◽  
Author(s):  
Liangfu Cao ◽  
Lianli Gao ◽  
Jingkuan Song ◽  
Fumin Shen ◽  
Yuan Wang
Keyword(s):  
Image Retrieval ◽  
Large Scale ◽  
Deep Hashing ◽  
Large Scale Image Retrieval
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