Visual quality assessment by machine learning [electronic resource] / Long Xu, Weisi Lin, C.-C. Jay Kuo.
2015
Q325.5
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Visual quality assessment by machine learning [electronic resource] / Long Xu, Weisi Lin, C.-C. Jay Kuo.
Author
Xu, Long, author.
ISBN
9789812874689 electronic book
9812874682 electronic book
9789812874672
9812874682 electronic book
9789812874672
Published
Singapore : Springer, 2015.
Language
English
Description
1 online resource (xiv, 132 pages) : illustrations.
Item Number
10.1007/978-981-287-468-9 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
The book encompasses the state-of-the-art visual quality assessment (VQA) and learning based visual quality assessment (LB-VQA) by providing a comprehensive overview of the existing relevant methods. It delivers the readers the basic knowledge, systematic overview and new development of VQA. It also encompasses the preliminary knowledge of Machine Learning (ML) to VQA tasks and newly developed ML techniques for the purpose. Hence, firstly, it is particularly helpful to the beginner-readers (including research students) to enter into VQA field in general and LB-VQA one in particular. Secondly, new development in VQA and LB-VQA particularly are detailed in this book, which will give peer researchers and engineers new insights in VQA.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed May 14, 2015).
Series
SpringerBriefs in electrical and computer engineering. Signal processing.
Available in Other Form
Print version: 9789812874672
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources
Table of Contents
Introduction
Fundamental knowledges of machine learning
Image features and feature processing
Feature pooling by learning
Metrics fusion
Summary and remarks for future research.
Fundamental knowledges of machine learning
Image features and feature processing
Feature pooling by learning
Metrics fusion
Summary and remarks for future research.