Haiyan Fu
ABSTRACT
With the growth of images on the Internet, plenty of hashing methods are developed to handle the large-scale image retrieval task. Hashing methods map data from high dimension to compact codes, so that they can effectively cope with complicated image features. However, the quantization process of hashing results in unescapable information loss. As a consequence, it is a challenge to measure the similarity between images with generated binary codes. The latest works usually focus on learning deep features and hashing functions simultaneously to preserve the similarity between images, while the similarity metric is fixed. In this paper, we propose a Rank-embedded Hashing (ReHash) algorithm where the ranking list is automatically optimized together with the feedback of the supervised hashing. Specifically, ReHash jointly trains the metric learning and the hashing codes in an end-to-end model. In this way, the similarity between images are enhanced by the ranking process. Meanwhile, the ranking results are an additional supervision for the hashing function learning as well. Extensive experiments show that our ReHash outperforms the state-of-the-art hashing methods for large-scale image retrieval.
- Song Bai, Peng Tang, Philip HS Torr, and Longin Jan Latecki. 2019. Re-ranking via metric fusion for object retrieval and person re-identification. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 740--749.Google Scholar
Cross Ref
- Bing-Kun Bao, Changsheng Xu, Weiqing Min, and Mohammod Shamim Hossain. 2015. Cross-platform emerging topic detection and elaboration from multimedia streams. ACM Transactions on Multimedia Computing, Communications, and Applications (TOMM), Vol. 11, 4 (2015), 54.Google Scholar
- Yue Cao, Mingsheng Long, Liu Bin, and Jianmin Wang. 2018. Deep Cauchy Hashing for Hamming Space Retrieval. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Google ScholarCross Ref
- Yue Cao, Mingsheng Long, Jianmin Wang, Han Zhu, and Qingfu Wen. 2016. Deep Quantization Network for Efficient Image Retrieval. In Proceedings of the Association for the Advance of Artificial Intelligence.Google ScholarCross Ref
- Zhangjie Cao, Mingsheng Long, Jianmin Wang, and S. Philip Yu. 2017. HashNet: Deep Learning to Hash by Continuation. In Proceedings of the IEEE International Conference on Computer Vision.Google Scholar
- Ken Chatfield, Karen Simonyan, Andrea Vedaldi, and Andrew Zisserman. 2014. Return of the devil in the details: Delving deep into convolutional nets. In British Machine Vision Conference.Google ScholarCross Ref
- Tat-Seng Chua, Jinhui Tang, Richang Hong, Haojie Li, Zhiping Luo, and Yantao Zheng. 2009. NUS-WIDE: a real-world web image database from National University of Singapore. 48:1--48:9.Google Scholar
- Venice Erin Liong, Jiwen Lu, Gang Wang, Pierre Moulin, and Jie Zhou. 2015. Deep hashing for compact binary codes learning. In Proceedings of the IEEE conference on computer vision and pattern recognition. 2475--2483.Google ScholarCross Ref
- Haiyan Fu, Xiangwei Kong, and Zhenfan Wang. 2016a. Binary code reranking method with weighted hamming distance. Multimedia Tools and Applications, Vol. 75, 3 (2016), 1391--1408.Google ScholarDigital Library
- Haiyan Fu, Hanguang Zhao, Xiangwei Kong, and Xianbo Zhang. 2016b. BHoG: binary descriptor for sketch-based image retrieval. Multimedia Systems, Vol. 22, 1 (2016), 127--136.Google ScholarDigital Library
- Lianli Gao, Jingkuan Song, Fuhao Zou, Dongxiang Zhang, and Jie Shao. 2015. Scalable multimedia retrieval by deep learning hashing with relative similarity learning. In Proceedings of the 23rd ACM international conference on Multimedia. ACM, 903--906.Google ScholarDigital Library
- Weifeng Ge. 2018. Deep metric learning with hierarchical triplet loss. In Proceedings of the European Conference on Computer Vision (ECCV). 269--285.Google ScholarDigital Library
- Yunchao Gong, Svetlana Lazebnik, Albert Gordo, and Florent Perronnin. 2012. Iterative quantization: A procrustean approach to learning binary codes for large-scale image retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 35, 12 (2012), 2916--2929.Google ScholarDigital Library
- Raia Hadsell, Sumit Chopra, and Yann LeCun. 2006. Dimensionality reduction by learning an invariant mapping. In 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Vol. 2. IEEE, 1735--1742.Google ScholarDigital Library
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition. 770--778.Google ScholarCross Ref
- Qing-Yuan Jiang and Wu-Jun Li. 2015. Scalable graph hashing with feature transformation. In Twenty-Fourth International Joint Conference on Artificial Intelligence.Google Scholar
- Qing-Yuan Jiang and Wu-Jun Li. 2018. Asymmetric deep supervised hashing. In Thirty-Second AAAI Conference on Artificial Intelligence.Google ScholarCross Ref
- Yu-Gang Jiang, Jun Wang, and Shih-Fu Chang. 2011. Lost in binarization: query-adaptive ranking for similar image search with compact codes. In Proceedings of the 1st ACM International Conference on Multimedia Retrieval. ACM, 16.Google ScholarDigital Library
- Wang-Cheng Kang, Wu-Jun Li, and Zhi-Hua Zhou. 2016. Column sampling based discrete supervised hashing. In Thirtieth AAAI conference on artificial intelligence.Google ScholarDigital Library
- Weihao Kong and Wu-Jun Li. 2012. Isotropic hashing. In Advances in neural information processing systems. 1646--1654.Google Scholar
- Alex Krizhevsky. 2009. Learning multiple layers of features from tiny images. Technical Report. University of Toronto.Google Scholar
- Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton. 2012. Imagenet classification with deep convolutional neural networks. In Advances in neural information processing systems. 1097--1105.Google Scholar
- Hanjiang Lai, Yan Pan, Ye Liu, and Shuicheng Yan. 2015. Simultaneous feature learning and hash coding with deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition. 3270--3278.Google ScholarCross Ref
- Ning Li, Chao Li, Cheng Deng, Xianglong Liu, and Xinbo Gao. 2018b. Deep joint semantic-embedding hashing. In Proceedings of theTwenty-Seventh International Joint Conference on Artifiial Intelligence.Google ScholarCross Ref
- Peng Li, Meng Wang, Jian Cheng, Changsheng Xu, and Hanqing Lu. 2012. Spectral hashing with semantically consistent graph for image indexing. IEEE Transactions on Multimedia, Vol. 15, 1 (2012), 141--152.Google ScholarDigital Library
- Qiang Li, Haiyan Fu, Xiangwei Kong, and Qi Tian. 2018a. Deep hashing with top similarity preserving for image retrieval. Multimedia Tools and Applications, Vol. 77, 18 (2018), 24121--24141.Google ScholarDigital Library
- Wu-Jun Li, Sheng Wang, and Wang-Cheng Kang. 2016. Feature learning based deep supervised hashing with pairwise labels. In Proceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence.Google ScholarDigital Library
- Guosheng Lin, Chunhua Shen, Qinfeng Shi, Anton Van den Hengel, and David Suter. 2014b. Fast supervised hashing with decision trees for high-dimensional data. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 1963--1970.Google ScholarDigital Library
- Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick. 2014a. Microsoft coco: Common objects in context. In European conference on computer vision. Springer, 740--755.Google ScholarCross Ref
- Bin Liu, Yue Cao, Mingsheng Long, Jianmin Wang, and Jingdong Wang. 2018. Deep Triplet Quantization. In Proceedings of the ACM international conference on Multimedia.Google ScholarDigital Library
- Wei Liu, Cun Mu, Sanjiv Kumar, and Shih-Fu Chang. 2014b. Discrete graph hashing. In Advances in neural information processing systems. 3419--3427.Google Scholar
- Wei Liu, Jun Wang, Rongrong Ji, Yu-Gang Jiang, and Shih-Fu Chang. 2012. Supervised hashing with kernels. In 2012 IEEE Conference on Computer Vision and Pattern Recognition. IEEE, 2074--2081.Google ScholarDigital Library
- Zhen Liu, Houqiang Li, Wengang Zhou, Ruizhen Zhao, and Qi Tian. 2014a. Contextual hashing for large-scale image search. IEEE Transactions on Image Processing, Vol. 23, 4 (2014), 1606--1614.Google ScholarDigital Library
- Xuan Ma, Bing-Kun Bao, Lingling Yao, and Changsheng Xu. 2019. Multimodal Latent Factor Model with Language Constraint for Predicate Detection. In 2019 IEEE International Conference on Image Processing (ICIP). IEEE, 4454--4458.Google Scholar
- Behnam Neyshabur, Nati Srebro, Ruslan R Salakhutdinov, Yury Makarychev, and Payman Yadollahpour. 2013. The power of asymmetry in binary hashing. In Advances in Neural Information Processing Systems. 2823--2831.Google Scholar
- Fudong Nian, Bing-Kun Bao, Teng Li, and Changsheng Xu. 2017. Multi-modal knowledge representation learning via webly-supervised relationships mining. In Proceedings of the 25th ACM international conference on Multimedia. ACM, 411--419.Google ScholarDigital Library
- Hyun Oh Song, Yu Xiang, Stefanie Jegelka, and Silvio Savarese. 2016. Deep metric learning via lifted structured feature embedding. In Proceedings of the IEEE conference on computer vision and pattern recognition. 4004--4012.Google ScholarCross Ref
- Fumin Shen, Xin Gao, Li Liu, Yang Yang, and Heng Tao Shen. 2017. Deep asymmetric pairwise hashing. In Proceedings of the 25th ACM international conference on Multimedia. 1522--1530.Google ScholarDigital Library
- Fumin Shen, Chunhua Shen, Wei Liu, and Heng Tao Shen. 2015. Supervised discrete hashing. In Proceedings of the IEEE conference on computer vision and pattern recognition. 37--45.Google ScholarCross Ref
- Fumin Shen, Yan Xu, Li Liu, Yang Yang, Zi Huang, and Heng Tao Shen. 2018. Unsupervised deep hashing with similarity-adaptive and discrete optimization. IEEE transactions on pattern analysis and machine intelligence, Vol. 40, 12 (2018), 3034--3044.Google ScholarDigital Library
- Xiaoshuang Shi, Fuyong Xing, Kaidi Xu, Manish Sapkota, and Lin Yang. 2017. Asymmetric discrete graph hashing. In Thirty-First AAAI Conference on Artificial Intelligence.Google ScholarDigital Library
- Kihyuk Sohn. 2016. Improved deep metric learning with multi-class n-pair loss objective. In Advances in neural information processing systems. 1857--1865.Google Scholar
- Junyi Wang, Bing-Kun Bao, and Changsheng Xu. 2019 a. Sentiment-Aware Multi-modal Recommendation on Tourist Attractions. In International Conference on Multimedia Modeling. Springer, 3--16.Google Scholar
- Jianfeng Wang, Jingdong Wang, Nenghai Yu, and Shipeng Li. 2013. Order preserving hashing for approximate nearest neighbor search. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 133--142.Google ScholarDigital Library
- Xinshao Wang, Yang Hua, Elyor Kodirov, Guosheng Hu, Romain Garnier, and Neil M Robertson. 2019 c. Ranked list loss for deep metric learning. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 5207--5216.Google ScholarCross Ref
- Xiaofang Wang, Yi Shi, and Kris M Kitani. 2016. Deep supervised hashing with triplet labels. In Asian conference on computer vision. Springer, 70--84.Google Scholar
- Yinghao Wang, Chen Chen, Jiong Wang, and Yingying Zhu. 2019 b. Learning Discriminative Features for Image Retrieval. In Proceedings of the 2019 on International Conference on Multimedia Retrieval. ACM, 96--104.Google ScholarDigital Library
- Yair Weiss, Antonio Torralba, and Rob Fergus. 2009. Spectral hashing. In Advances in neural information processing systems. 1753--1760.Google Scholar
- Xinyi Xu, Yanhua Yang, Cheng Deng, and Feng Zheng. 2019. Deep asymmetric metric learning via rich relationship mining. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 4076--4085.Google ScholarCross Ref
- Lei Zhang, Yongdong Zhang, Jinhui Tang, Xiaoguang Gu, Jintao Li, and Qi Tian. 2013. Topology preserving hashing for similarity search. In Proceedings of the 21st ACM international conference on Multimedia. ACM, 123--132.Google ScholarDigital Library
- Peichao Zhang, Wei Zhang, Wu-Jun Li, and Minyi Guo. 2014. Supervised hashing with latent factor models. In Proceedings of the 37th international ACM SIGIR conference on Research & development in information retrieval. ACM, 173--182.Google ScholarDigital Library
- Fang Zhao, Yongzhen Huang, Liang Wang, and Tieniu Tan. 2015. Deep semantic ranking based hashing for multi-label image retrieval. In Proceedings of the IEEE conference on computer vision and pattern recognition. 1556--1564.Google Scholar
- Han Zhu, Mingsheng Long, Jianmin Wang, and Yue Cao. 2016. Deep Hashing Network for Efficient Similarity Retrieval. In Proceedings of the Association for the Advance of Artificial Intelligence.Google ScholarCross Ref
- Yueting Zhuang, Yang Liu, Fei Wu, Yin Zhang, and Jian Shao. 2011. Hypergraph spectral hashing for similarity search of social image. In Proceedings of the 19th ACM international conference on Multimedia. ACM, 1457--1460.Google ScholarDigital Library
Index Terms
- Rank-embedded Hashing for Large-scale Image Retrieval
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