scholarly journals Learning Binary Hash Codes for Large-Scale Image Search

2013 ◽  
pp. 49-87 ◽  
Author(s):  
Kristen Grauman ◽  
Rob Fergus
Keyword(s):  
Large Scale ◽  
Image Search ◽  
Hash Codes

BSIFT: Toward Data-Independent Codebook for Large Scale Image Search

2015 ◽  
Vol 24 (3) ◽  
pp. 967-979 ◽  
Author(s):  
Wengang Zhou ◽  
Houqiang Li ◽  
Richang Hong ◽  
Yijuan Lu ◽  
Qi Tian
Keyword(s):  
Large Scale ◽  
Image Search

Hybrid-Indexing Multi-type Features for Large-Scale Image Search

2015 ◽  
pp. 446-460 ◽  
Author(s):  
Qingjun Luo ◽  
Shiliang Zhang ◽  
Tiejun Huang ◽  
Wen Gao ◽  
Qi Tian
Keyword(s):  
Large Scale ◽  
Image Search

scholarly journals Indexing of the CNN features for the large scale image search

2018 ◽  
Vol 77 (24) ◽  
pp. 32107-32131 ◽  
Author(s):  
Ruoyu Liu ◽  
Shikui Wei ◽  
Yao Zhao ◽  
Yi Yang
Keyword(s):  
Large Scale ◽  
Image Search

scholarly journals Coupled CycleGAN: Unsupervised Hashing Network for Cross-Modal Retrieval

2019 ◽  
Vol 33 ◽  
pp. 176-183 ◽  
Author(s):  
Chao Li ◽  
Cheng Deng ◽  
Lei Wang ◽  
De Xie ◽  
Xianglong Liu
Keyword(s):  
Large Scale ◽  
State Of The Art ◽  
The State ◽  
Storage Cost ◽  
Common Representation ◽  
Benchmark Datasets ◽  
Query Efficiency ◽  
Hash Codes

In recent years, hashing has attracted more and more attention owing to its superior capacity of low storage cost and high query efficiency in large-scale cross-modal retrieval. Benefiting from deep leaning, continuously compelling results in cross-modal retrieval community have been achieved. However, existing deep cross-modal hashing methods either rely on amounts of labeled information or have no ability to learn an accuracy correlation between different modalities. In this paper, we proposed Unsupervised coupled Cycle generative adversarial Hashing networks (UCH), for cross-modal retrieval, where outer-cycle network is used to learn powerful common representation, and inner-cycle network is explained to generate reliable hash codes. Specifically, our proposed UCH seamlessly couples these two networks with generative adversarial mechanism, which can be optimized simultaneously to learn representation and hash codes. Extensive experiments on three popular benchmark datasets show that the proposed UCH outperforms the state-of-the-art unsupervised cross-modal hashing methods.

SIFT match verification by geometric coding for large-scale partial-duplicate web image search

2013 ◽  
Vol 9 (1) ◽  
pp. 1-18 ◽  
Author(s):  
Wengang Zhou ◽  
Houqiang Li ◽  
Yijuan Lu ◽  
Qi Tian
Keyword(s):  
Large Scale ◽  
Image Search

scholarly journals Multi-Feature Discrete Collaborative Filtering for Fast Cold-Start Recommendation

2020 ◽  
Vol 34 (01) ◽  
pp. 270-278
Author(s):  
Yang Xu ◽  
Lei Zhu ◽  
Zhiyong Cheng ◽  
Jingjing Li ◽  
Jiande Sun
Keyword(s):  
Collaborative Filtering ◽  
Large Scale ◽  
Feature Fusion ◽  
Computation Time ◽  
Cold Start ◽  
User Preferences ◽  
Low Rank ◽  
Specific Content ◽  
Hash Codes ◽  
Content Feature

Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects.

scholarly journals Simultaneous Feature Aggregating and Hashing for Large-Scale Image Search

2017 ◽  
Author(s):  
Thanh-Toan Do ◽  
Dang-Khoa Le Tan ◽  
Trung T. Pham ◽  
Ngai-Man Cheung
Keyword(s):  
Large Scale ◽  
Image Search
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