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Dynamic Multi-view Hashing for Online Image Retrieval

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

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Dynamic Multi-view Hashing for Online Image Retrieval. / Xie, Liang; Shen, Jialie; Han, Jungong et al.
Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. ed. / Carles Sierra. Melbourne: IJCAI, 2017. p. 3133-3139.

Research output: Contribution in Book/Report/Proceedings - With ISBN/ISSNConference contribution/Paperpeer-review

Harvard

Xie, L, Shen, J, Han, J, Zhu, L & Shao, L 2017, Dynamic Multi-view Hashing for Online Image Retrieval. in C Sierra (ed.), Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. IJCAI, Melbourne, pp. 3133-3139, IJCAI17, 21/08/17. https://doi.org/10.24963/ijcai.2017/437

APA

Xie, L., Shen, J., Han, J., Zhu, L., & Shao, L. (2017). Dynamic Multi-view Hashing for Online Image Retrieval. In C. Sierra (Ed.), Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (pp. 3133-3139). IJCAI. https://doi.org/10.24963/ijcai.2017/437

Vancouver

Xie L, Shen J, Han J, Zhu L, Shao L. Dynamic Multi-view Hashing for Online Image Retrieval. In Sierra C, editor, Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. Melbourne: IJCAI. 2017. p. 3133-3139 doi: 10.24963/ijcai.2017/437

Author

Xie, Liang ; Shen, Jialie ; Han, Jungong et al. / Dynamic Multi-view Hashing for Online Image Retrieval. Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence. editor / Carles Sierra. Melbourne : IJCAI, 2017. pp. 3133-3139

Bibtex

@inproceedings{ccc98fdc00fe45329cd10effe8702596,
title = "Dynamic Multi-view Hashing for Online Image Retrieval",
abstract = "Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.",
author = "Liang Xie and Jialie Shen and Jungong Han and Lei Zhu and Ling Shao",
year = "2017",
month = aug,
day = "19",
doi = "10.24963/ijcai.2017/437",
language = "English",
pages = "3133--3139",
editor = "Carles Sierra",
booktitle = "Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence",
publisher = "IJCAI",
note = "IJCAI17 ; Conference date: 21-08-2017 Through 25-08-2017",

}

RIS

TY - GEN

T1 - Dynamic Multi-view Hashing for Online Image Retrieval

AU - Xie, Liang

AU - Shen, Jialie

AU - Han, Jungong

AU - Zhu, Lei

AU - Shao, Ling

PY - 2017/8/19

Y1 - 2017/8/19

N2 - Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.

AB - Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.

U2 - 10.24963/ijcai.2017/437

DO - 10.24963/ijcai.2017/437

M3 - Conference contribution/Paper

SP - 3133

EP - 3139

BT - Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence

A2 - Sierra, Carles

PB - IJCAI

CY - Melbourne

T2 - IJCAI17

Y2 - 21 August 2017 through 25 August 2017

ER -