Abstract
Advanced hashing technique is essential in large s-cale online image retrieval and organization, where image contents are frequently changed. While traditional multi-view hashing method has achieve promising effectiveness, its batch-based learning based scheme largely leads to very expensive updating cost. Meanwhile, existing online hashing scheme generally focuses on single-view data. Good effectiveness can not be expected when searching over real online images, which typically have multiple views. Further, both types of hashing methods only can generate hash codes with fixed length. Thus they have limited capability on comprehensive characterization of streaming image data. 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 in the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on t-wo real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.
| Original language | English |
|---|---|
| Title of host publication | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
| Editors | Carles Sierra |
| Place of Publication | Melbourne; Australia |
| Publisher | International Joint Conferences on Artificial Intelligence |
| Pages | 3133-3139 |
| Number of pages | 7 |
| ISBN (Electronic) | 9780999241103 |
| DOIs | |
| Publication status | Published - 1 Jan 2017 |
| Externally published | Yes |
| Event | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 - Melbourne, Australia Duration: 19 Aug 2017 → 25 Aug 2017 |
Conference
| Conference | 26th International Joint Conference on Artificial Intelligence, IJCAI 2017 |
|---|---|
| Country | Australia |
| City | Melbourne |
| Period | 19/08/17 → 25/08/17 |
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Dynamic multi-view hashing for online image retrieval. / Xie, Liang; Shen, Jialie; Han, Jungong; Zhu, Lei; Shao, Ling.
26th International Joint Conference on Artificial Intelligence, IJCAI 2017. ed. / Carles Sierra. Melbourne; Australia : International Joint Conferences on Artificial Intelligence, 2017. p. 3133-3139.Research output: Chapter in Book/Report/Conference proceeding › Conference Paper published in Proceedings › Research › peer-review
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 - 2017/1/1
Y1 - 2017/1/1
N2 - Advanced hashing technique is essential in large s-cale online image retrieval and organization, where image contents are frequently changed. While traditional multi-view hashing method has achieve promising effectiveness, its batch-based learning based scheme largely leads to very expensive updating cost. Meanwhile, existing online hashing scheme generally focuses on single-view data. Good effectiveness can not be expected when searching over real online images, which typically have multiple views. Further, both types of hashing methods only can generate hash codes with fixed length. Thus they have limited capability on comprehensive characterization of streaming image data. 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 in the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on t-wo real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.
AB - Advanced hashing technique is essential in large s-cale online image retrieval and organization, where image contents are frequently changed. While traditional multi-view hashing method has achieve promising effectiveness, its batch-based learning based scheme largely leads to very expensive updating cost. Meanwhile, existing online hashing scheme generally focuses on single-view data. Good effectiveness can not be expected when searching over real online images, which typically have multiple views. Further, both types of hashing methods only can generate hash codes with fixed length. Thus they have limited capability on comprehensive characterization of streaming image data. 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 in the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on t-wo real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.
UR - http://www.scopus.com/inward/record.url?scp=85031930241&partnerID=8YFLogxK
U2 - 10.24963/ijcai.2017/437
DO - 10.24963/ijcai.2017/437
M3 - Conference Paper published in Proceedings
SP - 3133
EP - 3139
BT - 26th International Joint Conference on Artificial Intelligence, IJCAI 2017
A2 - Sierra, Carles
PB - International Joint Conferences on Artificial Intelligence
CY - Melbourne; Australia
ER -