Highly-Economized Multi-View Binary Compression for Scalable Image Clustering
Harbin Institute of Technology (Shenzhen)（哈尔滨工业大学（深圳））；The University of Queensland（昆士兰大学）；Inception Institute of Artificial Intelligence（起源人工智能研究院）；ETH Zurich（苏黎世联邦理工学院）；University of Electronic Science and Technology of China（电子科技大学）
Zheng Zhang, Li Liu, Jie Qin, Fan Zhu, Fumin Shen, Yong Xu, Ling Shao, Heng Tao Shen
How to economically cluster large-scale multi-view images is a long-standing problem in computer vision. To tackle this challenge, we introduce a novel approach named Highly-economized Scalable Image Clustering (HSIC) that radically surpasses conventional image clustering methods via binary compression. We intuitively unify the binary representation learning and efficient binary cluster structure learning into a joint framework. In particular, common binary representations are learned by exploiting both sharable and individual information across multiple views to capture their underlying correlations. Meanwhile, cluster assignment with robust binary centroids is also performed via effective discrete optimization under L21-norm constraint. By this means, heavy continuous-valued Euclidean distance computations can be successfully reduced by efficient binary XOR operations during the clustering procedure. To our best knowledge, HSIC is the first binary clustering work specifically designed for scalable multi-view image clustering. Extensive experimental results on four large-scale image datasets show that HSIC consistently outperforms the state-of-the-art approaches, whilst significantly reducing computational time and memory footprint.
Hierarchical Spatio-Channel Clustering for Efficient Model Compression in Medical Image Analysis
Department of Mathematics, Rhodes University（罗德斯大学数学系）；Department of Statistics, Rhodes University（罗德斯大学统计系）；National Institute for Theoretical and Computational Sciences（国家理论与计算科学研究所）；College of Health Solutions, Arizona State University（亚利桑那州立大学健康解决方案学院）；Department of Statistics, College of Natural and Computational Sciences, Addis Ababa University（亚的斯亚贝巴大学自然与计算科学学院统计系）；Department of Statistics, University of Pretoria（比勒陀利亚大学统计系）；Université Clermont Auvergne, Clermont Auvergne INP, CNRS, Institut Pascal（克莱蒙特-奥弗涅大学，克莱蒙特-奥弗涅INP，CNRS，帕西尔研究所）；Laboratoire MIA, La Rochelle Université（拉罗谢尔大学MIA实验室）
Performance Benchmarking and Optimisation of Clustering Algorithms for Local and Non-Local Similarity Measure in Medical Image Analysis
Department of Mathematics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa（数学系，罗德斯大学）；Department of Statistics, Rhodes University, PO Box 94, Makhanda, 6140, South Africa（统计系，罗德斯大学）；National Institute for Theoretical and Computational Sciences (NITheCS), Stellenbosch 7600, South Africa（理论与计算科学国家研究所）
MIS-HCC: Hierarchical Channel Clustering for Efficient Medical Image Segmentation
Yale University（耶鲁大学）；University of Florida（佛罗里达大学）；Wichita State University（威奇托州立大学）；Purdue University（普渡大学）；University of California, Berkeley（加利福尼亚大学伯克利分校）；Institute of Computing Technology, Chinese Academy of Sciences（中国科学院计算技术研究所）；Stevens Institute of Technology（史蒂文斯理工学院）；Zhejiang University（浙江大学）
GOLLIC: Learning Global Context beyond Patches for Lossless High-Resolution Image Compression
Huawei Hong Kong Research Center（华为香港研究中心）；The Hong Kong University of Science and Technology（香港科技大学）；HKUST Shenzhen-Hong Kong Collaborative Innovation Research Institute（香港科技大学深港协同创新研究院）
Image Splicing Detection, Localization and Attribution via JPEG Primary Quantization Matrix Estimation and Clustering
Institute of Information Science, Beijing Jiaotong University（北京交通大学信息科学研究所）；Beijing Key Laboratory of Advanced Information Science and Network Technology（北京市先进信息科学与网络技术重点实验室）；Department of Information Engineering and Mathematics, University of Siena（锡耶纳大学信息工程与数学系）
Segmentation of Natural Images by Texture and Boundary Compression
Coordinated Science Lab, University of Illinois（伊利诺伊大学协调科学实验室）；HRL Laboratories, LLC（HRL 有限责任公司实验室）；University of California, Berkeley（加州大学伯克利分校）；Microsoft Research Asia（微软亚洲研究院）
