{"type":"ScholarlyArticle","title":"DRLnet: Deep Difference Representation Learning Network and An Unsupervised Optimization Framework","authors":[{"name":"Puzhao Zhang","slug":"puzhao-zhang","given":"Puzhao","family":"Zhang","affiliation":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China , Xidian University , Xi'an 710071 , China"},{"name":"Maoguo Gong","slug":"maoguo-gong","given":"Maoguo","family":"Gong","affiliation":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China , Xidian University , Xi'an 710071 , China"},{"name":"Hui Zhang","slug":"hui-zhang","given":"Hui","family":"Zhang","affiliation":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China , Xidian University , Xi'an 710071 , China"},{"name":"Jia Liu","slug":"jia-liu","given":"Jia","family":"Liu","affiliation":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education of China , Xidian University , Xi'an 710071 , China"}],"abstract":"Change detection and analysis (CDA) is an important research topic in the joint interpretation of spatial-temporal remote sensing images. The core of CDA is to effectively represent the difference and measure the difference degree between bi-temporal images. In this paper, we propose a novel difference representation learning network (DRLnet) and an effective optimization framework without any supervision. Difference measurement, difference representation learning and unsupervised clustering are combined as a single model, i.e., DRLnet, which is driven to learn clustering-friendly and discriminative difference representations (DRs) for different types of changes. Further, DRLnet is extended into a recurrent learning framework to update and reuse\r\nlimited training samples and prevent the semantic gaps caused by the saltation in the number of change types from over-clustering stage to the desired one. Experimental results identify the effectiveness of the proposed framework.","doi":"10.24963/ijcai.2017/477","url":"https://ijcai.org/proceedings/2017/477","pdf":"https://ijcai.org/proceedings/2017/0477.pdf","markdown":"https://ijcai.org/proceedings/2017/477.md","bibtex":"https://ijcai.org/proceedings/2017/bibtex/477","volume":{"slug":"2017","title":"Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2017","url":"https://ijcai.org/proceedings/2017/","year":2017,"location":"Melbourne, Australia","dates":"19-25 August 2017","editors":"Carles Sierra","isbn":"978-0-9992411-0-3","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2017"},"section":"Machine Learning S-Z","track":"Main track","number":477,"pages":{"first":3413,"last":3419},"keywords":["Machine Learning: Machine Learning","Machine Learning: Unsupervised Learning","Machine Learning: Neural Networks","Machine Learning: Deep Learning"],"videos":[],"references":[{"position":1,"raw":"[Bazi et al., 2005] Yakoub Bazi, Lorenzo Bruzzone, and Farid Melgani. An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal sar images. IEEE Transactions on Geo- science and Remote Sensing, 43(4):874-887, 2005.","title":"An unsupervised approach based on the generalized gaussian model to automatic change detection in multitemporal sar images","authors":"Bazi","year":"2005","venue":"IEEE Transactions on Geoscience and Remote Sensing","doi":null,"url":null,"internal_url":null},{"position":2,"raw":"[Biernacki et al., 2000] Christophe Biernacki, Gilles Celeux, and Gérard Govaert. Assessing a mixture model for clus- tering with the integrated completed likelihood. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 22(7):719-725, 2000.","title":"Assessing a mixture model for clustering with the integrated completed likelihood","authors":"Christophe Biernacki, Gilles Celeux, Gérard Govaert","year":"2000","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":3,"raw":"[Chi et al., 2016] Mingmin Chi, Antonio Plaza, Jón Atli Benediktsson, Zhongyi Sun, Jinsheng Shen, and Yangy- ong Zhu. Big data for remote sensing: Challenges and op- portunities. Proceedings of the IEEE, 104(11):2207-2219, 2016.","title":"Big data for remote sensing: Challenges and opportunities","authors":"Chi","year":"2016","venue":"Proceedings of the IEEE","doi":null,"url":null,"internal_url":null},{"position":4,"raw":"[Conneau et al., 2016] Alexis Conneau, Holger Schwenk, Loïc Barrault, and Yann Lecun. Very deep convolutional networks for natural language processing. arXiv preprint arXiv:1606.01781, 2016.","title":"Very deep convolutional networks for natural language processing","authors":"Alexis Conneau, Holger Schwenk, Loïc Barrault, Yann Lecun","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":5,"raw":"[Dash et al., 2016] Abhisek Dash, Sujoy Chatterjee, Tripti Prasad, and Malay Bhattacharyya. Image clustering with- out ground truth. arXiv preprint arXiv:1610.07758, 2016.","title":"Image clustering without ground truth","authors":"Abhisek Dash, Sujoy Chatterjee, Tripti Prasad, Malay Bhattacharyya","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":6,"raw":"[Dilokthanakul et al., 2016] Nat Dilokthanakul, Pedro AM Mediano, Marta Garnelo, Matthew CH Lee, Hugh Sal- imbeni, Kai Arulkumaran, and Murray Shanahan. Deep unsupervised clustering with gaussian mixture variational autoencoders. arXiv preprint arXiv:1611.02648, 2016.","title":"Deep unsupervised clustering with gaussian mixture variational autoencoders","authors":"Dilokthanakul","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":7,"raw":"[Gowda and Krishna, 1978] Chidananda Gowda and Garg Hari Krishna. Agglomerative clustering using the concept of mutual nearest neighbourhood. Pattern Recognition, 10(2):105-112, 1978.","title":"Agglomerative clustering using the concept of mutual nearest neighbourhood","authors":"Chidananda Gowda, Garg Hari, Krishna","year":"1978","venue":"Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":8,"raw":"[He et al., 2016] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recog- nition. In Proceedings of the 29th International Confer- ence on Computer Vision and Pattern Recognition, pages 770-778, Las Vegas, USA, June 2016.","title":"Deep residual learning for image recognition","authors":"Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun","year":"2016","venue":"Proceedings of the 29th International Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":9,"raw":"[Hinton and Salakhutdinov, 2006] Geoffrey Hinton and Rus- lan Salakhutdinov. Reducing the dimensionality of data with neural networks. Science, 313(5786):504-7, 2006.","title":"Geoffrey Hinton and Ruslan Salakhutdinov. Reducing the dimensionality of data with neural networks","authors":"Salakhutdinov Hinton","year":"2006","venue":"Science","doi":null,"url":null,"internal_url":null},{"position":10,"raw":"[Hong et al., 2016] Seunghoon Hong, Jonghyun Choi, Jan Feyereisl, Bohyung Han, and Larry Davis. Joint image clustering and labeling by matrix factorization. IEEE Transactions on Pattern Analysis and Machine Intelli- gence, 38(7):1411-1424, 2016.","title":"Joint image clustering and labeling by matrix factorization","authors":"Hong","year":"2016","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":11,"raw":"[Jiang et al., 2016] Zhuxi Jiang, Yin Zheng, Huachun Tan, Bangsheng Tang, and Hanning Zhou. Variational deep embedding: A generative approach to clustering. arXiv preprint arXiv:1611.05148, 2016.","title":"Variational deep embedding: A generative approach to clustering","authors":"Jiang","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":12,"raw":"[Krizhevsky et al., 2012] Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton. Imagenet classification with deep convolutional neural networks. In Proceedings of the 26th International Conference on Neural Information Process- ing Systems, pages 1097-1105, Lake Tahoe, USA, Decem- ber 2012.","title":"Imagenet classification with deep convolutional neural networks","authors":"Alex Krizhevsky, Ilya Sutskever, Geoffrey Hinton","year":"2012","venue":"Proceedings of the 26th International Conference on Neural Information Processing Systems","doi":null,"url":null,"internal_url":null},{"position":13,"raw":"[Marchetti et al., 2016] Pier Giorgio Marchetti, Pierre Soille, and Lorenzo Bruzzone. A special issue on big data from space for geoscience and remote sensing [from the guest editors]. IEEE Geoscience and Remote Sensing Magazine, 4(3):7-9, 2016.","title":"A special issue on big data from space for geoscience and remote sensing [from the guest editors","authors":"Marchetti","year":"2016","venue":"IEEE Geoscience and Remote Sensing Magazine","doi":null,"url":null,"internal_url":null},{"position":14,"raw":"[Nie et al., 2011] Feiping Nie, Zinan Zeng, Ivor W Tsang, Dong Xu, and Changshui Zhang. Spectral embedded clustering: A framework for in-sample and out-of-sample spectral clustering. IEEE Transactions on Neural Net- works, 22(11):1796-1808, 2011.","title":"Spectral embedded clustering: A framework for in-sample and out-of-sample spectral clustering","authors":"Nie","year":"2011","venue":"IEEE Transactions on Neural Networks","doi":null,"url":null,"internal_url":null},{"position":15,"raw":"[Palm, 2012] Rasmus Berg Palm. Prediction as a candidate for learning deep hierarchical models of data. Technical University of Denmark, 2012.","title":"Prediction as a candidate for learning deep hierarchical models of data","authors":"Rasmus Berg, Palm","year":"2012","venue":null,"doi":null,"url":null,"internal_url":null},{"position":16,"raw":"[Shao et al., 2016] Jing Shao, Chen-Change Loy, Kai Kang, and Xiaogang Wang. Slicing convolutional neural net- work for crowd video understanding. In Proceedings of the 29th International Conference on Computer Vision and Pattern Recognition, pages 5620-5628, Las Vegas, USA, June 2016.","title":"Slicing convolutional neural network for crowd video understanding","authors":"Jing Shao, Chen-Change Loy, Kai Kang, Xiaogang Wang","year":"2016","venue":"Proceedings of the 29th International Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":17,"raw":"[Shi and Malik, 2000] Jianbo Shi and Jitendra Malik. Nor- malized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8):888- 905, 2000.","title":"Normalized cuts and image segmentation","authors":"Jianbo Shi, Jitendra Malik","year":"2000","venue":"IEEE Transactions on Pattern Analysis and Machine Intelligence","doi":null,"url":null,"internal_url":null},{"position":18,"raw":"[Wang et al., 2016] Zhangyang Wang, Shiyu Chang, Jiayu Zhou, Meng Wang, and Thomas Huang. Learning a task- specific deep architecture for clustering. In Proceedings of the 2016 SIAM International Conference on Data Mining, pages 369-377. SIAM, 2016.","title":"Learning a taskspecific deep architecture for clustering","authors":"Wang","year":"2016","venue":"Proceedings of the 2016 SIAM International Conference on Data Mining","doi":null,"url":null,"internal_url":null},{"position":19,"raw":"[Xie et al., 2016] Junyuan Xie, Ross Girshick, and Al- i Farhadi. Unsupervised deep embedding for clustering analysis. In Proceedings of the 33rd International Confer- ence on Machine Learning, New York, USA, June 2016.","title":"Unsupervised deep embedding for clustering analysis","authors":"Junyuan Xie, Ross Girshick, Ali Farhadi","year":"2016","venue":"Proceedings of the 33rd International Conference on Machine Learning","doi":null,"url":null,"internal_url":null},{"position":20,"raw":"[Yang et al., 2016a] Bo Yang, Xiao Fu, Nicholas Sidiropou- los, and Mingyi Hong. Towards k-means-friendly spaces: Simultaneous deep learning and clustering. arXiv preprint arXiv:1610.04794, 2016.","title":"Towards k-means-friendly spaces: Simultaneous deep learning and clustering","authors":"Yang","year":"2016","venue":null,"doi":null,"url":null,"internal_url":null},{"position":21,"raw":"[Yang et al., 2016b] Jianwei Yang, Devi Parikh, and Dhruv Batra. Joint unsupervised learning of deep representations and image clusters. In Proceedings of the 29th Interna- tional Conference on Computer Vision and Pattern Recog- nition, pages 5147-5156, Las Vegas, USA, June 2016.","title":"Joint unsupervised learning of deep representations and image clusters","authors":"Yang","year":"2016","venue":"Proceedings of the 29th International Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":22,"raw":"[You et al., 2016] Quanzeng You, Hailin Jin, Zhaowen Wang, Chen Fang, and Jiebo Luo. Image captioning with semantic attention. In Proceedings of the 29th Interna- tional Conference on Computer Vision and Pattern Recog- nition, pages 4651-4659, Las Vegas, USA, June 2016.","title":"Image captioning with semantic attention","authors":"You","year":"2016","venue":"Proceedings of the 29th International Conference on Computer Vision and Pattern Recognition","doi":null,"url":null,"internal_url":null},{"position":23,"raw":"[Zhang et al., 2016a] Liangpei Zhang, Xia Gui-Song, Tianfu Wu, Liang Lin, and Xue Cheng Tai. Deep learning for remote sensing image understanding. Journal of Sensors, 2016.","title":"Deep learning for remote sensing image understanding","authors":"Zhang","year":"2016","venue":"Journal of Sensors","doi":null,"url":null,"internal_url":null},{"position":24,"raw":"[Zhang et al., 2016b] Liangpei Zhang, Lefei Zhang, and Bo Du. Deep learning for remote sensing data: A tech- nical tutorial on the state of the art. IEEE Geoscience and Remote Sensing Magazine, 4(2):22-40, 2016.","title":"Deep learning for remote sensing data: A technical tutorial on the state of the art","authors":"Zhang","year":"2016","venue":"IEEE Geoscience and Remote Sensing Magazine","doi":null,"url":null,"internal_url":null}],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}