{"type":"ScholarlyArticle","title":"Dynamic Multi-View Hashing for Online Image Retrieval","authors":[{"name":"Liang Xie","slug":"liang-xie","given":"Liang","family":"Xie","affiliation":"Wuhan University of Technology , China"},{"name":"Jialie Shen","slug":"jialie-shen","given":"Jialie","family":"Shen","affiliation":"Northumbria University , United Kingdom"},{"name":"Jungong Han","slug":"jungong-han","given":"Jungong","family":"Han","affiliation":"Lancaster University , United Kingdom"},{"name":"Lei Zhu","slug":"lei-zhu","given":"Lei","family":"Zhu","affiliation":"The University of Queensland , Australia"},{"name":"Ling Shao","slug":"ling-shao","given":"Ling","family":"Shao","affiliation":"University of East Anglia , United Kingdom"}],"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.","doi":"10.24963/ijcai.2017/437","url":"https://ijcai.org/proceedings/2017/437","pdf":"https://ijcai.org/proceedings/2017/0437.pdf","markdown":"https://ijcai.org/proceedings/2017/437.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/437","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":437,"pages":{"first":3133,"last":3139},"keywords":["Machine Learning: Multi-instance/Multi-label/Multi-view learning","Machine Learning: Online Learning"],"videos":[],"references":[{"position":1,"raw":"[Caicedo et al., 2012] Juan C Caicedo, Jaafar BenAbdallah, Fabio A González, and Olfa Nasraoui. 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