{"type":"ScholarlyArticle","title":"A Probabilistic Code Balance Constraint with Compactness and Informativeness Enhancement for Deep Supervised Hashing","authors":[{"name":"Qi Zhang","slug":"qi-zhang","given":"Qi","family":"Zhang","affiliation":"Data Science Lab , University of Technology Sydney; School of Computer Science and Technology , Beijing Institute of Technology; Deepblue Academy of Sciences"},{"name":"Liang Hu","slug":"liang-hu","given":"Liang","family":"Hu","affiliation":"College of Electronic and Information Engineering , Tongji University; Deepblue Academy of Sciences"},{"name":"Longbing Cao","slug":"longbing-cao","given":"Longbing","family":"Cao","affiliation":"Data Science Lab , University of Technology Sydney"},{"name":"Chongyang Shi","slug":"chongyang-shi","given":"Chongyang","family":"Shi","affiliation":"School of Computer Science and Technology , Beijing Institute of Technology"},{"name":"Shoujin Wang","slug":"shoujin-wang","given":"Shoujin","family":"Wang","affiliation":"School of Computing Technologies , RMIT University"},{"name":"Dora D. Liu","slug":"dora-d-liu","given":"Dora D.","family":"Liu","affiliation":"Deepblue Academy of Sciences"}],"abstract":"Building on deep representation learning, deep supervised hashing has achieved promising performance in tasks like similarity retrieval. However, conventional code balance constraints (i.e., bit balance and bit uncorrelation) imposed on avoiding overfitting and improving hash code quality are unsuitable for deep supervised hashing owing to their inefficiency and impracticality of simultaneously learning deep data representations and hash functions. To address this issue, we propose probabilistic code balance constraints on deep supervised hashing to force each hash code to conform to a discrete uniform distribution. Accordingly, a Wasserstein regularizer aligns the distribution of generated hash codes to a uniform distribution. Theoretical analyses reveal that the proposed constraints form a general deep hashing framework for both bit balance and bit uncorrelation and maximizing the mutual information between data input and their corresponding hash codes. Extensive empirical analyses on two benchmark datasets further demonstrate the enhancement of compactness and informativeness of hash codes for deep supervised hash to improve retrieval performance (code available at: https://github.com/mumuxi/dshwr).","doi":"10.24963/ijcai.2022/230","url":"https://ijcai.org/proceedings/2022/230","pdf":"https://ijcai.org/proceedings/2022/0230.pdf","markdown":"https://ijcai.org/proceedings/2022/230.md","bibtex":"https://ijcai.org/proceedings/2022/bibtex/230","volume":{"slug":"2022","title":"Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2022","url":"https://ijcai.org/proceedings/2022/","year":2022,"location":"Vienna","dates":"23-29 July 2022","editors":"Luc De Raedt","isbn":"978-1-956792-00-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2022"},"section":"Computer Vision","track":"Main Track","number":230,"pages":{"first":1651,"last":1657},"keywords":["Data Mining: Information Retrieval","Data Mining: Applications","Computer Vision: Image and Video retrieval"],"videos":[{"label":"Video #1","url":"https://slideslive.com/38986753","length_sec":85,"provider":"slideslive"},{"label":"Video #2","url":"https://slideslive.com/38984896","length_sec":336,"provider":"slideslive"}],"references":[{"position":1,"raw":"[Belghazi et al., 2018] Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R. Devon Hjelm, and Aaron C. Courville. Mutual informa- tion neural estimation. In ICML, volume 80, pages 530- 539, 2018.","title":"Mutual information neural estimation","authors":"Mohamed Ishmael Belghazi, Aristide Baratin, Sai Rajeswar, Sherjil Ozair, Yoshua Bengio, R Devon Hjelm, Aaron C Courville","year":"2018","venue":"ICML","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Cao et al., 2017] Zhangjie Cao, Mingsheng Long, Jianmin Wang, and Philip S. Yu. Hashnet: Deep learning to hash by continuation. In ICCV, pages 5609-5618, 2017.","title":"Hashnet: Deep learning to hash by continuation","authors":"Zhangjie Cao, Mingsheng Long, Jianmin Wang, Philip S Yu","year":"2017","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Cao et al., 2018] Yue Cao, Mingsheng Long, Bin Liu, and Jianmin Wang. Deep cauchy hashing for hamming space retrieval. In CVPR, pages 1229-1237, 2018.","title":"Deep cauchy hashing for hamming space retrieval","authors":"Yue Cao, Mingsheng Long, Bin Liu, Jianmin Wang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Chen et al., 2019] Yudong Chen, Zhihui Lai, Yujuan Ding, Kaiyi Lin, and Wai Keung Wong. Deep supervised hashing with anchor graph. In ICCV, pages 9795-9803, 2019.","title":"Deep supervised hashing with anchor graph","authors":"Yudong Chen, Zhihui Lai, Yujuan Ding, Kaiyi Lin, Wai Keung Wong","year":"2019","venue":"ICCV","doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Datar et al., 2004] Mayur Datar, Nicole Immorlica, Piotr Indyk, and Vahab S. Mirrokni. Locality-sensitive hashing scheme based on p-stable distributions. In Symposium on Computational Geometry, pages 253-262. ACM, 2004.","title":"Locality-sensitive hashing scheme based on p-stable distributions","authors":"Mayur Datar, Nicole Immorlica, Piotr Indyk, Vahab S Mirrokni","year":"2004","venue":"Symposium on Computational Geometry","doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dizaji et al., 2018] Kamran Ghasedi Dizaji, Feng Zheng, Najmeh Sadoughi, Yanhua Yang, Cheng Deng, and Heng Huang. Unsupervised deep generative adversarial hashing network. In CVPR, pages 3664-3673, 2018.","title":"Unsupervised deep generative adversarial hashing network","authors":"Kamran Ghasedi Dizaji, Feng Zheng, Najmeh Sadoughi, Yanhua Yang, Cheng Deng, Heng Huang","year":"2018","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gattupalli et al., 2019] Vijetha Gattupalli, Yaoxin Zhuo, and Baoxin Li. Weakly supervised deep image hashing through tag embeddings. In CVPR, pages 10375-10384, 2019.","title":"Weakly supervised deep image hashing through tag embeddings","authors":"Gattupalli","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[Jiang and Li, 2018] Qing-Yuan Jiang and Wu-Jun Li. Asymmetric deep supervised hashing. In AAAI, pages 3342-3349, 2018.","title":"Asymmetric deep supervised hashing","authors":"Li ; Qing-Yuan Jiang, Wu-Jun Jiang, Li","year":"2018","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Jiang et al., 2018] Qing-Yuan Jiang, Xue Cui, and Wu-Jun Li. Deep discrete supervised hashing. IEEE Trans. Image Process., 27(12):5996-6009, 2018.","title":"Deep discrete supervised hashing","authors":"Qing-Yuan Jiang, Xue Cui, Wu-Jun Li","year":"2018","venue":"IEEE Trans. Image Process","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton. Imagenet classification with deep convolutional neural networks. In NIPS, pages 1106- 1114, 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey E Hinton","year":"2012","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Kuhn, 1955] Harold W Kuhn. The hungarian method for the assignment problem. Naval research logistics quarterly, 2(1-2):83-97, 1955.","title":"The hungarian method for the assignment problem","authors":"Harold W Kuhn","year":"1955","venue":"Naval research logistics quarterly","doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Li et al., 2017] Qi Li, Zhenan Sun, Ran He, and Tieniu Tan. Deep supervised discrete hashing. In NIPS, pages 2482- 2491, 2017.","title":"Deep supervised discrete hashing","authors":"Qi Li, Zhenan Sun, Ran He, Tieniu Tan","year":"2017","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Liu et al., 2012] Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. Supervised hashing with ker- nels. In CVPR, pages 2074-2081, 2012.","title":"Supervised hashing with kernels","authors":"Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, Shih-Fu Chang","year":"2012","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Liu et al., 2014] Wei Liu, Cun Mu, Sanjiv Kumar, and Shih- Fu Chang. Discrete graph hashing. In NIPS, pages 3419- 3427, 2014.","title":"Discrete graph hashing","authors":"Wei Liu, Cun Mu, Sanjiv Kumar, Shih-Fu Chang","year":"2014","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Liu et al., 2018] Bin Liu, Yue Cao, Mingsheng Long, Jian- min Wang, and Jingdong Wang. Deep triplet quantization. In ACM Multimedia, pages 755-763, 2018.","title":"Deep triplet quantization","authors":"Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, Jingdong Wang","year":"2018","venue":"ACM Multimedia","doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Liu et al., 2019] Haomiao Liu, Ruiping Wang, Shiguang Shan, and Xilin Chen. Deep supervised hashing for fast image retrieval. Int. J. Comput. Vis., 127(9):1217-1234, 2019.","title":"Deep supervised hashing for fast image retrieval","authors":"Liu","year":"2019","venue":"Int. J. Comput. Vis","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Luo et al., 2020] Xiao Luo, Chong Chen, Huasong Zhong, Hao Zhang, Minghua Deng, Jianqiang Huang, and Xian- sheng Hua. A survey on deep hashing methods. CoRR, abs/2003.03369, 2020.","title":"A survey on deep hashing methods","authors":"Xiao Luo, Chong Chen, Huasong Zhong, Hao Zhang, Minghua Deng, Jianqiang Huang, Xiansheng Hua","year":"2020","venue":null,"doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Shi et al., 2017] Xiaoshuang Shi, Fuyong Xing, Kaidi Xu, Manish Sapkota, and Lin Yang. Asymmetric discrete graph hashing. In AAAI, pages 2541-2547, 2017.","title":"Asymmetric discrete graph hashing","authors":"Xiaoshuang Shi, Fuyong Xing, Kaidi Xu, Manish Sapkota, Lin Yang","year":"2017","venue":"AAAI","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Su et al., 2018] Shupeng Su, Chao Zhang, Kai Han, and Yonghong Tian. Greedy hash: Towards fast optimization for accurate hash coding in CNN. In NeurIPS, pages 806- 815, 2018.","title":"Greedy hash: Towards fast optimization for accurate hash coding in CNN","authors":"Su","year":"2018","venue":"NeurIPS","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Wang et al., 2012] Jun Wang, Sanjiv Kumar, and Shih-Fu Chang. Semi-supervised hashing for large-scale search. IEEE Trans. Pattern Anal. Mach. Intell., 34(12):2393- 2406, 2012.","title":"Semi-supervised hashing for large-scale search","authors":"Jun Wang, Sanjiv Kumar, Shih-Fu Chang","year":"2012","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Wang et al., 2016] Jun Wang, Wei Liu, Sanjiv Kumar, and Shih-Fu Chang. Learning to hash for indexing big data - A survey. Proceedings of the IEEE, 104(1):34-57, 2016.","title":"Learning to hash for indexing big data -A survey","authors":"Jun Wang, Wei Liu, Sanjiv Kumar, Shih-Fu Chang","year":"2016","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[Wang et al., 2018] Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, and Heng Tao Shen. A survey on learn- ing to hash. IEEE Trans. Pattern Anal. Mach. Intell., 40(4):769-790, 2018.","title":"A survey on learning to hash","authors":"Jingdong Wang, Ting Zhang, Jingkuan Song, Nicu Sebe, Heng Tao Shen","year":"2018","venue":"IEEE Trans. Pattern Anal. Mach. Intell","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Wang et al., 2020] Ruikui Wang, Ruiping Wang, Shishi Qiao, Shiguang Shan, and Xilin Chen. Deep position- aware hashing for semantic continuous image retrieval. In WACV, pages 2482-2491, 2020.","title":"Deep positionaware hashing for semantic continuous image retrieval","authors":"Wang","year":"2020","venue":"WACV","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Wang et al., 2021] Xunguang Wang, Zheng Zhang, Baoyuan Wu, Fumin Shen, and Guangming Lu. Prototype- supervised adversarial network for targeted attack of deep hashing. In CVPR, pages 16357-16366, 2021.","title":"Prototypesupervised adversarial network for targeted attack of deep hashing","authors":"Wang","year":"2021","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":25,"raw":"[Weiss et al., 2008] Yair Weiss, Antonio Torralba, and Robert Fergus. Spectral hashing. In NIPS, pages 1753- 1760, 2008.","title":"Spectral hashing","authors":"Yair Weiss, Antonio Torralba, Robert Fergus","year":"2008","venue":"NIPS","doi":null,"url":null,"internal_url":null},{"position":26,"raw":"[Wu et al., 2019] Dayan Wu, Qi Dai, Jing Liu, Bo Li, and Weiping Wang. Deep incremental hashing network for ef- ficient image retrieval. In CVPR, pages 9069-9077, 2019.","title":"Deep incremental hashing network for efficient image retrieval","authors":"Dayan Wu, Qi Dai, Jing Liu, Bo Li, Weiping Wang","year":"2019","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":27,"raw":"[Yuan et al., 2020] Li Yuan, Tao Wang, Xiaopeng Zhang, Francis E. H. Tay, Zequn Jie, Wei Liu, and Jiashi Feng. Central similarity quantization for efficient image and video retrieval. In CVPR, pages 3080-3089, 2020.","title":"Central similarity quantization for efficient image and video retrieval","authors":"Li Yuan, Tao Wang, Xiaopeng Zhang, Francis E H Tay, Zequn Jie, Wei Liu, Jiashi Feng","year":"2020","venue":"CVPR","doi":null,"url":null,"internal_url":null},{"position":28,"raw":"[Zhu et al., 2016] Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. Deep hashing network for efficient similarity retrieval. In AAAI, pages 2415-2421, 2016.","title":"Deep hashing network for efficient similarity retrieval","authors":"Han Zhu, Mingsheng Long, Jianmin Wang, Yue Cao","year":"2016","venue":"AAAI","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}