Visual Pattern Discovery and Recognition

Hongxing Wang received his B.S. and M.S. degrees from Chongqing University, China, and his Ph.D. degree from Nanyang Technological University, Singapore. He is currently a faculty member at the School of Software Engineering, Chongqing University. Before joining Chongqing University, he worked as a research fellow/associate at the School of Electrical and Electronic Engineering (EEE) at Nanyang Technological University, and as a visiting student at The Institute of Scientific and Industrial Research (ISIR), Osaka University, Japan. His research interests include computer vision, pattern recognition, and machine learning. Chaoqun Weng received his B.E. degree in Computer Science and Technology from Nankai University, China, in 2010. He is currently pursuing his Ph.D. degree at the School of Electrical & Electronics Engineering, Nanyang Technological University, Singapore. His research interests include computer vision and machine learning. JunsongYuan received his Ph.D. from Northwestern University and M.Eng. from the National University of Singapore. Before that, he graduated from the Special Class for the Gifted Young of Huazhong University of Science and Technology in China. He is currently an associate professor and program director of video analytics at the School of Electrical and Electronics Engineering, Nanyang Technological University (NTU). He serves as guest editor of International Journal of Computer Vision (IJCV), and is currently an associate editor of IEEE Trans. on Image Processing (T-IP), IEEE Trans. on Circuits and Systems for Video Technology (T-CSVT) and The Visual Computer journal (TVC). He also serves as area chair of various conferences including CVPR/ACCV/WACV/ICPR/ICME. He received Best Paper Award from IEEE Trans. on Multimedia, the Doctoral Spotlight Award from IEEE Conf. on Computer Vision and Pattern Recognition (CVPR'09), a Nanyang Assistant Professorship from NTU, and Outstanding EECS Ph.D. Thesis award from Northwestern University.

1. Introduction.- 2. Context-Aware Discovery of Visual Co-occurrence Patterns.- 3.  Hierarchical Sparse Coding for Visual Co-occurrence Discovery.- 4. Feature Co-occurrence for Visual Labeling.- 5. Visual Clustering with Minimax Feature Fusion.- 6. Conclusion.