{"type":"ScholarlyArticle","title":"Quantized Correlation Hashing for Fast Cross-Modal Search","authors":[{"name":"Botong Wu","slug":"botong-wu","given":"Botong","family":"Wu","affiliation":"† School of Information Science and Technology , Sun Yat-sen University , China; ‡ Nat'l Eng. Lab. for Video Technology , Cooperative Medianet Innovation Center , Sch'l of EECS , Peking University ♦"},{"name":"Qiang Yang","slug":"qiang-yang-2","given":"Qiang","family":"Yang","affiliation":"† School of Information Science and Technology , Sun Yat-sen University , China"},{"name":"Wei-Shi Zheng","slug":"wei-shi-zheng","given":"Wei-Shi","family":"Zheng","affiliation":"† School of Information Science and Technology , Sun Yat-sen University , China; § Collaborative Innovation Center of High Performance Computing , National University of Defense Technology Guangdong Provincial Key Laboratory of Computational Science Microsoft Research Asia , China"},{"name":"Yizhou Wang","slug":"yizhou-wang","given":"Yizhou","family":"Wang","affiliation":"‡ Nat'l Eng. Lab. for Video Technology , Cooperative Medianet Innovation Center , Sch'l of EECS , Peking University ♦"},{"name":"Jingdong Wang","slug":"jingdong-wang","given":"Jingdong","family":"Wang","affiliation":""}],"abstract":"Cross-modal hashing is designed to facilitate fast search across domains. In this work, we present a cross-modal hashing approach, called quantized correlation hashing (QCH), which takes into consideration the quantization loss over domains and the relation between domains. Unlike previous approaches that separate the optimization of the quantizer independent of maximization of domain correlation, our approach simultaneously optimizes both processes. The underlying relation between the domains that describes the same objects is established via maximizing the correlation between the hash codes across the domains. The resulting multi-modal objective function is transformed to a unimodal formalization, which is optimized through an alternative procedure. Experimental results on three real world datasets demonstrate that our approach outperforms the state-of-the-art multi-modal hashing methods.","doi":null,"url":"https://ijcai.org/proceedings/2015/554","pdf":"https://ijcai.org/proceedings/2015/0554.pdf","markdown":"https://ijcai.org/proceedings/2015/554.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/554","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Special Track on Machine Learning","track":"Special Track on Machine Learning","number":554,"pages":{"first":3946,"last":3951},"keywords":[],"videos":[],"references":[{"position":1,"raw":"[Bronstein et al., 2010] Michael M Bronstein, Alexander M Bronstein, Fabrice Michel, and Nikos Paragios. 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