{"type":"ScholarlyArticle","title":"Semantic Structure-based Unsupervised Deep Hashing","authors":[{"name":"Erkun Yang","slug":"erkun-yang","given":"Erkun","family":"Yang","affiliation":"School of Electronic Engineering , Xidian University , Xi'an 710071 , China"},{"name":"Cheng Deng","slug":"cheng-deng","given":"Cheng","family":"Deng","affiliation":"School of Electronic Engineering , Xidian University , Xi'an 710071 , China"},{"name":"Tongliang Liu","slug":"tongliang-liu","given":"Tongliang","family":"Liu","affiliation":"UBTECH Sydney AI Centre , SIT , FEIT , University of Sydney , Australia"},{"name":"Wei Liu","slug":"wei-liu","given":"Wei","family":"Liu","affiliation":"Tencent AI Lab , Shenzhen , China"},{"name":"Dacheng Tao","slug":"dacheng-tao","given":"Dacheng","family":"Tao","affiliation":"UBTECH Sydney AI Centre , SIT , FEIT , University of Sydney , Australia"}],"abstract":"Hashing is becoming increasingly popular for approximate nearest neighbor searching in massive databases due to its storage and search efficiency. Recent supervised hashing methods, which usually construct semantic similarity matrices to guide hash code learning using label information, have shown promising results. However, it is relatively difficult to capture and utilize the semantic relationships between points in unsupervised settings. To address this problem, we propose a novel unsupervised deep framework called Semantic Structure-based unsupervised Deep Hashing (SSDH). We first empirically study the deep feature statistics, and find that the distribution of the cosine distance for point pairs can be estimated by two half Gaussian distributions. Based on this observation, we construct the semantic structure by considering points with distances obviously smaller than the others as semantically similar and points with distances obviously larger than the others as semantically dissimilar. We then design a deep architecture and a pair-wise loss function to preserve this semantic structure in Hamming space. Extensive experiments show that SSDH significantly outperforms current state-of-the-art methods.","doi":"10.24963/ijcai.2018/148","url":"https://ijcai.org/proceedings/2018/148","pdf":"https://ijcai.org/proceedings/2018/0148.pdf","markdown":"https://ijcai.org/proceedings/2018/148.md","bibtex":"https://ijcai.org/proceedings/2018/bibtex/148","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":"Computer Vision","track":"Main track","number":148,"pages":{"first":1064,"last":1070},"keywords":["Multidisciplinary Topics and Applications: Information Retrieval","Computer Vision: Recognition: Detection, Categorization, Indexing, Matching, Retrieval, Semantic Interpretation"],"videos":[],"references":[{"position":1,"raw":"[Abadi et al., 2016] Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin, et al. Tensorflow: Large-scale machine learn- ing on heterogeneous distributed systems. arXiv preprint arXiv:1603.04467, 2016.","title":"Tensorflow: Large-scale machine learning on heterogeneous distributed systems","authors":"Martín Abadi, Ashish Agarwal, Paul Barham, Eugene Brevdo, Zhifeng Chen, Craig Citro, Greg S Corrado, Andy Davis, Jeffrey Dean, Matthieu Devin","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Andoni and Indyk, 2006] Alexandr Andoni and Piotr Indyk. Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In Foundations of Computer Science, 2006. FOCS'06. 47th Annual IEEE Symposium on, pages 459-468. IEEE, 2006.","title":"Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions","authors":"Alexandr Andoni, Piotr Indyk","year":"2006","venue":"FOCS'06. 47th Annual IEEE Symposium on","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Dai et al., 2017] Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, and Le Song. Stochastic generative hashing. arXiv preprint arXiv:1701.02815, 2017.","title":"Stochastic generative hashing","authors":"Bo Dai, Ruiqi Guo, Sanjiv Kumar, Niao He, Le Song","year":"2017","venue":null,"doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Deng et al., 2009] Jia Deng, Wei Dong, Richard Socher, Li- Jia Li, Kai Li, and Li Fei-Fei. Imagenet: A large-scale hierarchical image database. In Computer Vision and Pat- tern Recognition, 2009. CVPR 2009. IEEE Conference on, pages 248-255. IEEE, 2009.","title":"Imagenet: A large-scale hierarchical image database","authors":"Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, Li Fei-Fei","year":"2009","venue":"Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Deng et al., 2018] Cheng Deng, Zhaojia Chen, Xianglong Liu, Xinbo Gao, and Dacheng Tao. Triplet-based deep hashing network for cross-modal retrieval. IEEE Transac- tions on Image Processing, 2018.","title":"Triplet-based deep hashing network for cross-modal retrieval","authors":"Cheng Deng, Zhaojia Chen, Xianglong Liu, Xinbo Gao, Dacheng Tao","year":"2018","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Girshick et al., 2014] Ross Girshick, Jeff Donahue, Trevor Darrell, and Jitendra Malik. Rich feature hierarchies for accurate object detection and semantic segmentation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 580-587, 2014.","title":"Rich feature hierarchies for accurate object detection and semantic segmentation","authors":"Ross Girshick, Jeff Donahue, Trevor Darrell, Jitendra Malik","year":"2014","venue":"Proceedings of the IEEE conference on computer vision and pattern recognition","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gong et al., 2013] Yunchao Gong, Svetlana Lazebnik, Al- bert Gordo, and Florent Perronnin. Iterative quantization: A procrustean approach to learning binary codes for large- scale image retrieval. IEEE Transactions on Pattern Anal- ysis and Machine Intelligence, 35(12):2916-2929, 2013.","title":"Iterative quantization: A procrustean approach to learning binary codes for largescale image retrieval","authors":"Gong","year":"2013","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Gui et al., 2017] Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, and Tieniu Tan. Fast supervised discrete hashing. IEEE Transactions on Pattern Analysis and Ma- chine Intelligence, 2017.","title":"Fast supervised discrete hashing","authors":"Jie Gui, Tongliang Liu, Zhenan Sun, Dacheng Tao, Tieniu Tan","year":"2017","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Guttman, 1984] Antonin Guttman. R-trees: A dynamic in- dex structure for spatial searching, volume 14. ACM, 1984.","title":"R-trees: A dynamic index structure for spatial searching","authors":"Antonin Guttman","year":"1984","venue":null,"doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Heo et al., 2012] Jae-Pil Heo, Youngwoon Lee, Junfeng He, Shih-Fu Chang, and Sung-Eui Yoon. Spherical hashing. In Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on, pages 2957-2964. IEEE, 2012.","title":"Spherical hashing","authors":"Jae-Pil Heo, Youngwoon Lee, Junfeng He, Shih-Fu Chang, Sung-Eui Yoon","year":"2012","venue":"Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jin et al., 2014] Zhongming Jin, Cheng Li, Yue Lin, and Deng Cai. Density sensitive hashing. IEEE transactions on cybernetics, 44(8):1362-1371, 2014.","title":"Density sensitive hashing","authors":"Zhongming Jin, Cheng Li, Yue Lin, Deng Cai","year":"2014","venue":"IEEE transactions on cybernetics","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Krizhevsky and Hinton, 2011] Alex Krizhevsky and Geof- frey E Hinton. Using very deep autoencoders for content- based image retrieval. In ESANN, 2011.","title":"Using very deep autoencoders for contentbased image retrieval","authors":"Alex Krizhevsky, Geoffrey E Hinton","year":"2011","venue":"ESANN","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Li et al., 2018] Chao Li, Cheng Deng, Ning Li, Wei Liu, Xinbo Gao, and Dacheng Tao. Self-supervised adversarial hashing networks for cross-modal retrieval. arXiv preprint arXiv:1804.01223, 2018.","title":"Self-supervised adversarial hashing networks for cross-modal retrieval","authors":"Chao Li, Cheng Deng, Ning Li, Wei Liu, Xinbo Gao, Dacheng Tao","year":"2018","venue":null,"doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Lin et al., 2016] Kevin Lin, Jiwen Lu, Chu-Song Chen, and Jie Zhou. Learning compact binary descriptors with unsu- pervised deep neural networks. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 1183-1192, 2016.","title":"Learning compact binary descriptors with unsupervised deep neural networks","authors":"Kevin Lin, Jiwen Lu, Chu-Song Chen, Jie Zhou","year":"2016","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2011] Wei Liu, Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Hashing with graphs. In Proceedings of the 28th international conference on machine learning (ICML-11), pages 1-8, 2011.","title":"Hashing with graphs","authors":"Wei Liu, Jun Wang, Sanjiv Kumar, Shih-Fu Chang","year":"2011","venue":"Proceedings of the 28th international conference on machine learning (ICML-11)","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2012] W. Liu, J. Wang, R. Ji, Y. Jiang, and S.-F. Chang. Supervised hashing with kernels. In CVPR, pages 2074-2081, 2012.","title":"Supervised hashing with kernels","authors":"W Liu, J Wang, R Ji, Y Jiang, S.-F Chang","year":"2012","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Liu et al., 2014] Xianglong Liu, Junfeng He, Cheng Deng, and Bo Lang. Collaborative hashing. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recog- nition, pages 2139-2146, 2014.","title":"Collaborative hashing","authors":"Xianglong Liu, Junfeng He, Cheng Deng, Bo Lang","year":"2014","venue":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Liu et al., 2016] Xianglong Liu, Cheng Deng, Bo Lang, Dacheng Tao, and Xuelong Li. Query-adaptive reciprocal hash tables for nearest neighbor search. IEEE Transactions on Image Processing, 25(2):907-919, 2016.","title":"Query-adaptive reciprocal hash tables for nearest neighbor search","authors":"Xianglong Liu, Cheng Deng, Bo Lang, Dacheng Tao, Xuelong Li","year":"2016","venue":"IEEE Transactions on Image Processing","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Lowe, 2004] David G Lowe. Distinctive image features from scale-invariant keypoints. International journal of computer vision, 60(2):91-110, 2004.","title":"Distinctive image features from scale-invariant keypoints","authors":"David G Lowe","year":"2004","venue":"International journal of computer vision","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Ma et al., 2017] Chao Ma, Chen Gong, Yun Gu, Jie Yang, and Deying Feng. Shiss: Supervised hashing with infor- mative set selection. Pattern Recognition Letters, 2017.","title":"Shiss: Supervised hashing with informative set selection","authors":"Chao Ma, Chen Gong, Yun Gu, Jie Yang, Deying Feng","year":"2017","venue":"Pattern Recognition Letters","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Salakhutdinov and Hinton, 2009] Ruslan Salakhutdinov and Geoffrey Hinton. Semantic hashing. International Journal of Approximate Reasoning, 50(7):969-978, 2009.","title":"Ruslan Salakhutdinov and Geoffrey Hinton","authors":"Hinton Salakhutdinov","year":"2009","venue":"International Journal of Approximate Reasoning","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Simonyan and Zisserman, 2014] Karen Simonyan and An- drew Zisserman. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556, 2014.","title":"Very deep convolutional networks for large-scale image recognition","authors":"Karen Simonyan, Andrew Zisserman","year":"2014","venue":null,"doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Toth et al., 2004] Csaba D Toth, Joseph O'Rourke, and Ja- cob E Goodman. Handbook of discrete and computational geometry. CRC press, 2004.","title":"Handbook of discrete and computational geometry","authors":"D Csaba, Joseph O Toth, Jacob E 'rourke, Goodman","year":"2004","venue":null,"doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Weiss et al., 2009] Yair Weiss, Antonio Torralba, and Rob Fergus. Spectral hashing. In Advances in neural informa- tion processing systems, pages 1753-1760, 2009.","title":"Spectral hashing","authors":"Yair Weiss, Antonio Torralba, Rob Fergus","year":"2009","venue":"Advances in neural information processing systems","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Yang et al., 2017] Erkun Yang, Cheng Deng, Wei Liu, Xi- anglong Liu, Dacheng Tao, and Xinbo Gao. Pairwise rela- tionship guided deep hashing for cross-modal retrieval. In AAAI, 2017.","title":"Pairwise relationship guided deep hashing for cross-modal retrieval","authors":"Erkun Yang, Cheng Deng, Wei Liu, Xianglong Liu, Dacheng Tao, Xinbo Gao","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Yang et al., 2018] Erkun Yang, Cheng Deng, Chao Li, Wei Liu, Jie Li, and Dacheng Tao. Shared predictive cross- modal deep quantization. IEEE Transactions on Neural Networks and Learning Systems, 2018.","title":"Shared predictive crossmodal deep quantization","authors":"Erkun Yang, Cheng Deng, Chao Li, Wei Liu, Jie Li, Dacheng Tao","year":"2018","venue":"IEEE Transactions on Neural Networks and Learning Systems","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[You et al., 2017a] Shan You, Chang Xu, Yunhe Wang, Chao Xu, and Dacheng Tao. Privileged multi-label learning. arXiv preprint arXiv:1701.07194, 2017.","title":"Privileged multi-label learning","authors":"You","year":"2017","venue":null,"doi":null,"url":null,"internal_url":"/proceedings/2017/466"},{"position":28,"raw":"[You et al., 2017b] Shan You, Chang Xu, Chao Xu, and Dacheng Tao. Learning from multiple teacher networks. In Proceedings of the 23rd ACM SIGKDD International Con- ference on Knowledge Discovery and Data Mining, pages 1285-1294. ACM, 2017.","title":"Learning from multiple teacher networks","authors":"You","year":"2017","venue":"Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}