## Project## (Deep) Image Clustering
## Benchmark Results
## CitationsPlease cite the following papers, if you find the code is helpful. @inproceedings{zhang2012graph, ## (Deep) Image Clustering LiteratureW. Zhang, X. Wang, D. Zhao and X. Tang. Graph degree linkage: Agglomerative clustering on a directed graph. ECCV, 2012. W. Zhang, D. Zhao and X. Wang. Agglomerative clustering via maximum incremental path integral. Pattern Recognition, 46(11), pp.3056-3065, 2013. J. Yang, D. Parikh and D. Batra. Joint unsupervised learning of deep representations and image clusters. CVPR, 2016. [paper] [code] K.G. Dizaji, A. Herandi, C. Deng, W. Cai, H. Huang. Deep clustering via joint convolutional autoencoder embedding and relative entropy minimization. ICCV, 2017. [paper] [code] S.A. Shah and V. Koltun. Robust continuous clustering. PNAS, 114(37), pp.9814-9819, 2017. [paper] [code] S.A. Shah and V. Koltun. Deep Continuous Clustering. Arxiv, 2018. [paper] [code] X. Guo, E. Zhu, X. Liu and J. Yin. Deep embedded clustering with data augmentation. ACML, 2018. [paper] [code] X. Guo, L. Gao, X. Liu and J. Yin. Improved deep embedded clustering with local structure preservation. IJCAI, 2017. [paper] [code] J. Xie, R. Girshick and A. Farhadi. Unsupervised deep embedding for clustering analysis. ICML, 2016. [paper] Z. Jiang, Y. Zheng, H. Tan, B. Tang and H. Zhou. Variational deep embedding: An unsupervised and generative approach to clustering. IJCAI, 2017. [paper] [code] J. Chang, L. Wang, G. Meng, S. Xiang and C. Pan. Deep adaptive image clustering. ICCV, 2017. [paper] [code] F. Li, H. Qiao and B. Zhang. Discriminatively boosted image clustering with fully convolutional auto-encoders. Pattern Recognition, 83, 2017. [paper] Y. Ren, N. Wang, M. Li and Z. Xu. Deep Density-based Image Clustering. arXiv preprint arXiv:1812.04287, 2018. [paper] M. Caron, P. Bojanowski, A. Joulin and M. Douze. Deep clustering for unsupervised learning of visual features. ECCV, 2018. [paper] W. Hu, T. Miyato, S. Tokui, E. Matsumoto and M. Sugiyama. Learning Discrete Representations via Information Maximizing Self-Augmented Training. ICML, 2017. [paper] [code] U. Shaham, K. Stanton, H. Li, B. Nadler, R. Basri and Y. Kluger. SpectralNet: Spectral Clustering Using Deep Neural Networks. ICLR, 2018. [paper] [code] X. Guo, X. Liu, E. Zhu, X. Zhu, M. Li,X. Xu and J. Yin. Adaptive Self-paced Deep Clustering with Data Augmentation. IEEE TKDE, 2019. [paper] [code] X. Yang, C. Deng, F. Zheng, J. Yan and W. Liu. Deep Spectral Clustering using Dual Autoencoder Network. CVPR, 2019. [paper] K.G. Dizaji, X. Wang, C. Deng and H. Huang. Balanced Self-Paced Learning for Generative Adversarial Clustering Network. CVPR, 2019. [paper] X. Ji, J. F. Henriques and A. Vedaldi. Invariant information distillation for unsupervised image segmentation and clustering. ICCV, 2019. [paper] J. Wu, K. Long, F. Wang, C. Qian, C. Li, Z. Lin and H. Zha. Deep comprehensive correlation mining for image clustering. ICCV, 2019. [paper] [code] J. Huang, S. Gong and X. Zhu. Deep Semantic Clustering by Partition Confidence Maximisation. CVPR, 2020. [paper] [code]
## Many Other Applications of Graph Degree Linkage (GDL)GDL has been demonstrated as a good alternative of conventional clustering algorithms, such as k-means, DBSCAN, mean-shift, normalized cut, spectral clustering, linkage, ward, etc. Since its inventiona, GDL has been applied to many research areas, including: Computer vision: image clustering [1], face grouping [1, R17], image matching [1, R7], image segmentation [R3, R10], image search [R2], person re-identification [R4, R14], crowd analysis [R5, R6], saliency detection [R11], action recognition [R1]; Medical imaging [R8, R16]; Data mining [R12, R13], community detection [R9, R18], compiler optimization [R15]. [R1] Directed Acyclic Graph Kernels for Action Recognition. ICCV, 2013. [R2] Visual semantic complex network for web images. ICCV, 2013. [R3] Object co-segmentation based on directed graph clustering. VCIP, 2013. [R4] Learning mid-level filters for person re-identification. CVPR, 2014. [R5] Scene-independent group profiling in crowd. CVPR, 2014. [R6] Crowd tracking with dynamic evolution of group structures. ECCV, 2014. [R7] A Low-Dimensional Representation for Robust Partial Isometric Correspondences Computation. Graphical Models 76(2), March 2014. [R8] Hierarchical organization of the functional brain identified using floating aggregation of functional signals. ISBI, 2014. [R9] Considerations about multistep community detection. PAKDD Workshops 2014. [R10] Constrained directed graph clustering and segmentation propagation for multiple foregrounds cosegmentation. TCSVT, 2015. [R11] Saliency Detection Based on Graph-Structural Agglomerative Clustering. ACM MM, 2015. [R12] Spatial and temporal distribution and pollution assessment of trace metals in marine sediments in Oyster Bay, NSW, Australia. Bulletin of Environmental Contamination and Toxicology, 2015. [R13] Spatial distribution of sediment particles and trace element pollution within Gunnamatta Bay, Port Hacking, NSW, Australia. Regional Studies in Marine Science, 2015. [R14] Person re-identification based on hierarchical bipartite graph matching. ICIP, 2016. [R15] Micomp: Mitigating the compiler phase-ordering problem using optimization sub-sequences and machine learning. TACO, 2017. [R16] Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis. NeuroImage, 2018. [R17] Merge or not? learning to group faces via imitation learning. AAAI, 2018. [R18] CDlib: a Python Library to Extract, Compare and Evaluate Communities from Complex Networks. Applied Network Science Journal. 2019. CDlib - Community Discovery Library
`Footnote: The link in [1] (http://mmlab.ie.cuhk.edu.hk/research/gdl/) is not available any more.`
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