Two-step multi-view and multi-label learning with missing label via subspace learning

2021 ◽  
Vol 102 ◽  
pp. 107120
Author(s):  
Dawei Zhao ◽  
Qingwei Gao ◽  
Yixiang Lu ◽  
Dong Sun
Keyword(s):  
Subspace Learning

Automatically Outlier-Resisting Subspace Learning

2011 ◽  
Vol 30 (1) ◽  
pp. 176-179
Author(s):  
Yan-wei Pang ◽  
Zheng-kai Liu
Keyword(s):  
Subspace Learning

Multiview Shared Subspace Learning Across Speakers and Speech Commands

2019 ◽  
Author(s):  
Krishna Somandepalli ◽  
Naveen Kumar ◽  
Arindam Jati ◽  
Panayiotis Georgiou ◽  
Shrikanth Narayanan
Keyword(s):  
Subspace Learning

Learning task-driving affinity matrix for accurate multi-view clustering through tensor subspace learning

2021 ◽  
Vol 563 ◽  
pp. 290-308
Author(s):  
Haiyan Wang ◽  
Guoqiang Han ◽  
Junyu Li ◽  
Bin Zhang ◽  
Jiazhou Chen ◽  
...  
Keyword(s):  
Learning Task ◽  
Subspace Learning ◽  
Affinity Matrix ◽  
Tensor Subspace

scholarly journals Vehicle Counting in Video Sequences: An Incremental Subspace Learning Approach

Sensors ◽  
2019 ◽  
Vol 19 (13) ◽  
pp. 2848 ◽  
Author(s):  
Leonel Rosas-Arias ◽  
Jose Portillo-Portillo ◽  
Aldo Hernandez-Suarez ◽  
Jesus Olivares-Mercado ◽  
Gabriel Sanchez-Perez ◽  
...  
Keyword(s):  
Principal Component ◽  
Subspace Learning ◽  
Transport Systems ◽  
Video Sequences ◽  
Projected Image ◽  
The Road ◽  
Illumination Changes ◽  
On Line ◽  
Incremental Learning Method ◽  
On Line Learning

The counting of vehicles plays an important role in measuring the behavior patterns of traffic flow in cities, as streets and avenues can get crowded easily. To address this problem, some Intelligent Transport Systems (ITSs) have been implemented in order to count vehicles with already established video surveillance infrastructure. With this in mind, in this paper, we present an on-line learning methodology for counting vehicles in video sequences based on Incremental Principal Component Analysis (Incremental PCA). This incremental learning method allows us to identify the maximum variability (i.e., motion detection) between a previous block of frames and the actual one by using only the first projected eigenvector. Once the projected image is obtained, we apply dynamic thresholding to perform image binarization. Then, a series of post-processing steps are applied to enhance the binary image containing the objects in motion. Finally, we count the number of vehicles by implementing a virtual detection line in each of the road lanes. These lines determine the instants where the vehicles pass completely through them. Results show that our proposed methodology is able to count vehicles with 96.6% accuracy at 26 frames per second on average—dealing with both camera jitter and sudden illumination changes caused by the environment and the camera auto exposure.

scholarly journals Subspace learning-based graph regularized feature selection

2016 ◽  
Vol 112 ◽  
pp. 152-165 ◽  
Author(s):  
Ronghua Shang ◽  
Wenbing Wang ◽  
Rustam Stolkin ◽  
Licheng Jiao
Keyword(s):  
Feature Selection ◽  
Subspace Learning

Visual Tracking via Subspace Learning: A Discriminative Approach

2017 ◽  
Vol 126 (5) ◽  
pp. 515-536 ◽  
Author(s):  
Yao Sui ◽  
Yafei Tang ◽  
Li Zhang ◽  
Guanghui Wang
Keyword(s):  
Visual Tracking ◽  
Subspace Learning ◽  
Discriminative Approach
Sign in / Sign up

Export Citation Format

Share Document