A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions-Reference-Cited by-同舟云学术

A Review on Machine Learning Styles in Computer Vision—Techniques and Future Directions

Published:2022 Issue: Volume:10 Page:107293-107329
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mahadevkar Supriya V.¹,Khemani Bharti¹,Patil Shruti²^ORCID,Kotecha Ketan²^ORCID,Vora Deepali R.¹^ORCID,Abraham Ajith³^ORCID,Gabralla Lubna Abdelkareim⁴^ORCID

Affiliation:

1. Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

2. Symbiosis Centre for Applied Artificial Intelligence, Symbiosis Institute of Technology, Symbiosis International (Deemed University), Pune, India

3. Machine Intelligence Research Laboratories (MIR Laboratories), Auburn, WA, USA

4. Department of Computer Science and Information Technology, College of Applied, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia

Funder

Princess Nourah bint Abdulrahman University Researchers

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09903420.pdf?arnumber=9903420

Reference115 articles.

1. Facial Action Unit Detection Using Active Learning and an Efficient Non-linear Kernel Approximation

2. Active Learning with Gaussian Processes for Object Categorization

3. Not-so-supervised: A survey of semi-supervised, multi-instance, and transfer learning in medical image analysis

4. Image database retrieval with multiple-instance learning techniques

5. MILES: Multiple-Instance Learning via Embedded Instance Selection

Cited by 75 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Machine learning-driven feature importance appraisal of seismic parameters on tunnel damage and seismic fragility prediction;Engineering Applications of Artificial Intelligence;2024-11

2. From superpixels to foundational models: An overview of unsupervised and generalizable image segmentation;Computers & Graphics;2024-10

3. Estimating Forest Gross Primary Production Using Machine Learning, Light Use Efficiency Model, and Global Eddy Covariance Data;Forests;2024-09-13

4. How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review;Mayo Clinic Proceedings: Digital Health;2024-09

5. The Improvement of Density Peaks Clustering Algorithm and Its Application to Point Cloud Segmentation of LiDAR;Sensors;2024-09-01