{"type":"ScholarlyArticle","title":"SDMCH: Supervised Discrete Manifold-Embedded Cross-Modal Hashing","authors":[{"name":"Xin Luo","slug":"xin-luo","given":"Xin","family":"Luo","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Xiao-Ya Yin","slug":"xiao-ya-yin","given":"Xiao-Ya","family":"Yin","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Liqiang Nie","slug":"liqiang-nie","given":"Liqiang","family":"Nie","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Xuemeng Song","slug":"xuemeng-song","given":"Xuemeng","family":"Song","affiliation":"School of Computer Science and Technology , Shandong University; School of Software , Shandong University"},{"name":"Yongxin Wang","slug":"yongxin-wang","given":"Yongxin","family":"Wang","affiliation":"School of Computer Science and Technology , Shandong University"},{"name":"Xin-Shun Xu","slug":"xin-shun-xu","given":"Xin-Shun","family":"Xu","affiliation":"School of Computer Science and Technology , Shandong University"}],"abstract":"Cross-modal hashing methods have attracted considerable attention. Most pioneer approaches only preserve the neighborhood relationship by constructing the correlations among heterogeneous modalities. However, they neglect the fact that the high-dimensional data often exists on a low-dimensional manifold embedded in the ambient space and the relative proximity between the neighbors is also important. Although some methods leverage the manifold learning to generate the hash codes, most of them fail to explicitly explore the discriminative information in the class labels and discard the binary constraints during optimization, generating large quantization errors. To address these issues, in this paper, we present a novel cross-modal hashing method, named Supervised Discrete Manifold-Embedded Cross-Modal Hashing (SDMCH). It can not only exploit the non-linear manifold structure of data and construct the correlation among heterogeneous multiple modalities, but also fully utilize the semantic information. Moreover, the hash codes can be generated discretely by an iterative optimization algorithm, which can avoid the large quantization errors. Extensive experimental results on three benchmark datasets demonstrate that SDMCH outperforms ten state-of-the-art cross-modal hashing methods.","doi":"10.24963/ijcai.2018/349","url":"https://ijcai.org/proceedings/2018/349","pdf":"https://ijcai.org/proceedings/2018/0349.pdf","markdown":"https://ijcai.org/proceedings/2018/349.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/349","volume":{"slug":"2018","title":"Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2018","url":"https://ijcai.org/proceedings/2018/","year":2018,"location":"Stockholm","dates":"13-19 July 2018","editors":"Jérôme Lang","isbn":"978-0-9992411-2-7","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2018"},"section":"Machine Learning","track":"Main track","number":349,"pages":{"first":2518,"last":2524},"keywords":["Constraints and SAT: Constraint Optimisation","Machine Learning: Dimensionality Reduction and Manifold Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Multidisciplinary Topics and Applications: Information Retrieval","Machine Learning Applications: Applications of Supervised Learning"],"videos":[],"references":[{"position":1,"raw":"[Chua et al., 2009] Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. 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