{"type":"ScholarlyArticle","title":"Complementary Binary Quantization for Joint Multiple Indexing","authors":[{"name":"Qiang Fu","slug":"qiang-fu","given":"Qiang","family":"Fu","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Xu Han","slug":"xu-han","given":"Xu","family":"Han","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Xianglong Liu","slug":"xianglong-liu","given":"Xianglong","family":"Liu","affiliation":"State Key Lab of Software Development Environment , Beihang University , China"},{"name":"Jingkuan Song","slug":"jingkuan-song","given":"Jingkuan","family":"Song","affiliation":"Center for Future Media and School of Computer Science and Engineering , University of Electronic Science and Technology of China , China"},{"name":"Cheng Deng","slug":"cheng-deng","given":"Cheng","family":"Deng","affiliation":"School of Electronic Engineering , Xidian University , China"}],"abstract":"Building multiple hash tables has been proven a successful technique for indexing massive databases, which can guarantee a desired level of overall performance. However, existing hash based multi-indexing methods suffer from the heavy redundancy, without strong table complementarity and effective hash code learning. To address the problems, this paper proposes a complementary binary quantization (CBQ) method to jointly learning multiple hash tables. It exploits the power of incomplete binary coding based on prototypes to align the original space and the Hamming space, and further utilizes the nature of multi-indexing search to jointly reduce the quantization loss based on the prototype based hash function. Our alternating optimization adaptively discovers the complementary prototype sets and the corresponding code sets of a varying size in an efficient way, which together robustly approximate the data relations. Our method can be naturally generalized to the product space for long hash codes. Extensive experiments carried out on two popular large-scale tasks including Euclidean and semantic nearest neighbor search demonstrate that the proposed CBQ method enjoys the strong table complementarity and significantly outperforms the state-of-the-art, with up to 57.76\\% performance gains relatively.","doi":"10.24963/ijcai.2018/292","url":"https://ijcai.org/proceedings/2018/292","pdf":"https://ijcai.org/proceedings/2018/0292.pdf","markdown":"https://ijcai.org/proceedings/2018/292.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/292","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":292,"pages":{"first":2114,"last":2120},"keywords":["Machine Learning: Unsupervised Learning","Heuristic Search and Game Playing: Heuristic Search and Machine Learning","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation","Computer Vision: Computer Vision"],"videos":[],"references":[{"position":1,"raw":"[Cheng et al., 2014] Jian Cheng, Cong Leng, Jiaxiang Wu, Hainan Cui, and Hanqing Lu. 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