東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Visual quality assessment by machine...

Xu, Long.

Linked to FindBook

Google Book

Amazon

博客來

Visual quality assessment by machine learning

Record Type:	Electronic resources : Monograph/item
Title/Author:	Visual quality assessment by machine learning/ by Long Xu, Weisi Lin, C.-C. Jay Kuo.
Author:	Xu, Long.
other author:	Lin, Weisi.
Published:	Singapore :Springer Singapore : : 2015.,
Description:	xiv, 132 p. :ill., digital ;24 cm.
[NT 15003449]:	Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research.
Contained By:	Springer eBooks
Subject:	Machine learning. -
Online resource:	http://dx.doi.org/10.1007/978-981-287-468-9
ISBN:	9789812874689 (electronic bk.)

Visual quality assessment by machine learning
Xu, Long.

Visual quality assessment by machine learning[electronic resource] /by Long Xu, Weisi Lin, C.-C. Jay Kuo. - Singapore :Springer Singapore :2015. - xiv, 132 p. :ill., digital ;24 cm. - SpringerBriefs in electrical and computer engineering, Signal processing,2191-8112. - SpringerBriefs in electrical and computer engineering.Signal processing..

Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research.

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.

ISBN: 9789812874689 (electronic bk.)

Standard No.: 10.1007/978-981-287-468-9doiSubjects--Topical Terms:

533906
Machine learning.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Visual quality assessment by machine learning
LDR:02027nmm a2200349 a 4500 001 2006424
003 DE-He213
005 20151229163037.0
006 m d
007 cr nn 008maaau
008 160114s2015 si s 0 eng d
020 $a 9789812874689 (electronic bk.)
020 $a 9789812874672 (paper)
024 7 $a 10.1007/978-981-287-468-9 $2 doi
035 $a 978-981-287-468-9
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a TTBM $2 bicssc
072 7 $a UYS $2 bicssc
072 7 $a TEC008000 $2 bisacsh
072 7 $a COM073000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .X8 2015
100 1 $a Xu, Long. $3 2153327
245 1 0 $a Visual quality assessment by machine learning $h [electronic resource] / $c by Long Xu, Weisi Lin, C.-C. Jay Kuo.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2015.
300 $a xiv, 132 p. : $b ill., digital ; $c 24 cm.
490 1 $a SpringerBriefs in electrical and computer engineering, Signal processing, $x 2191-8112
505 0 $a Introduction -- Fundamental knowledges of machine learning -- Image features and feature processing -- Feature pooling by learning -- Metrics fusion -- Summary and remarks for future research.
520 $a 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.
650 0 $a Machine learning. $3 533906
650 0 $a Image processing $x Digital techniques. $3 532550
650 1 4 $a Engineering. $3 586835
650 2 4 $a Signal, Image and Speech Processing. $3 891073
650 2 4 $a Image Processing and Computer Vision. $3 891070
650 2 4 $a Computational Intelligence. $3 1001631
700 1 $a Lin, Weisi. $3 1530428
700 1 $a Kuo, C.-C. Jay. $3 923819
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer eBooks
830 0 $a SpringerBriefs in electrical and computer engineering. $p Signal processing. $3 2054749
856 4 0 $u http://dx.doi.org/10.1007/978-981-287-468-9
950 $a Engineering (Springer-11647)