Automatic Road Crack Detection Using Random Structured Forests zhiquan qi | 1071* | |
Robust twin support vector machine for pattern classification Z Qi, Y Tian, Y Shi Pattern recognition 46 (1), 305-316, 2013 | 346 | 2013 |
Nonparallel support vector machines for pattern classification Y Tian, Z Qi, X Ju, Y Shi, X Liu IEEE transactions on cybernetics 44 (7), 1067-1079, 2013 | 257 | 2013 |
Laplacian twin support vector machine for semi-supervised classification Z Qi, Y Tian, Y Shi Neural networks 35, 46-53, 2012 | 212 | 2012 |
Structural twin support vector machine for classification Z Qi, Y Tian, Y Shi Knowledge-based systems 43, 74-81, 2013 | 156 | 2013 |
Twin support vector machine with universum data Z Qi, Y Tian, Y Shi Neural Networks 36, 112-119, 2012 | 140 | 2012 |
Learning to incorporate structure knowledge for image inpainting J Yang, Z Qi, Y Shi Proceedings of the AAAI conference on artificial intelligence 34 (07), 12605 …, 2020 | 129 | 2020 |
Support vector machine classifier with truncated pinball loss X Shen, L Niu, Z Qi, Y Tian Pattern Recognition 68, 199-210, 2017 | 110 | 2017 |
Unsupervised anomaly segmentation via deep feature reconstruction Y Shi, J Yang, Z Qi Neurocomputing 424, 9-22, 2021 | 104 | 2021 |
When ensemble learning meets deep learning: a new deep support vector machine for classification Z Qi, B Wang, Y Tian, P Zhang Knowledge-Based Systems 107, 54-60, 2016 | 80 | 2016 |
Online multiple instance boosting for object detection Z Qi, Y Xu, L Wang, Y Song Neurocomputing 74 (10), 1769-1775, 2011 | 76 | 2011 |
Support vector regression for newspaper/magazine sales forecasting X Yu, Z Qi, Y Zhao Procedia Computer Science 17, 1055-1062, 2013 | 75 | 2013 |
Improved twin support vector machine Y Tian, X Ju, Z Qi, Y Shi Science China Mathematics 57, 417-432, 2014 | 68 | 2014 |
Pavement distress detection using random decision forests L Cui, Z Qi, Z Chen, F Meng, Y Shi Data Science: Second International Conference, ICDS 2015, Sydney, Australia …, 2015 | 63 | 2015 |
Efficient railway tracks detection and turnouts recognition method using HOG features Z Qi, Y Tian, Y Shi Neural Computing and Applications 23, 245-254, 2013 | 62 | 2013 |
A novel clustering-based image segmentation via density peaks algorithm with mid-level feature Y Shi, Z Chen, Z Qi, F Meng, L Cui Neural Computing and Applications 28, 29-39, 2017 | 58 | 2017 |
Dfr: Deep feature reconstruction for unsupervised anomaly segmentation J Yang, Y Shi, Z Qi arXiv preprint arXiv:2012.07122, 2020 | 55 | 2020 |
A survey on semantic segmentation B Li, Y Shi, Z Qi, Z Chen 2018 IEEE International Conference on Data Mining Workshops (ICDMW), 1233-1240, 2018 | 52 | 2018 |
Image segmentation via improving clustering algorithms with density and distance Z Chen, Z Qi, F Meng, L Cui, Y Shi Procedia Computer Science 55, 1015-1022, 2015 | 46 | 2015 |
支持向量机中的核参数选择问题 齐志泉, 田英杰, 徐志洁 控制工程 12 (4), 379-381, 2005 | 46* | 2005 |