國立高雄大學圖資館 |

Language: English

Back to Search results for [ subject:"Multimedia data mining." ]

Fundamentals of image data miningana...

SpringerLink (Online service)

Fundamentals of image data mininganalysis, features, classification and retrieval /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of image data miningby Dengsheng Zhang.
Reminder of title:	analysis, features, classification and retrieval /
Author:	Zhang, Dengsheng.
Published:	Cham :Springer International Publishing :2019.
Description:	xxxi, 314 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Multimedia data mining.
Online resource:	https://doi.org/10.1007/978-3-030-17989-2
ISBN:	9783030179892$q(electronic bk.)

Fundamentals of image data mininganalysis, features, classification and retrieval /
Zhang, Dengsheng.

Fundamentals of image data mininganalysis, features, classification and retrieval /[electronic resource] :by Dengsheng Zhang. - Cham :Springer International Publishing :2019. - xxxi, 314 p. :ill., digital ;24 cm. - Texts in computer science,1868-0941. - Texts in computer science..

Part I: Preliminaries -- Fourier Transform -- Windowed Fourier Transform -- Wavelet Transform -- Part II: Image Representation and Feature Extraction -- Color Feature Extraction -- Texture Feature Extraction -- Shape Representation -- Part III: Image Classification and Annotation -- Bayesian Classification -- Support Vector Machines -- Artificial Neural Networks -- Image Annotation with Decision Trees -- Part IV: Image Retrieval and Presentation -- Image Indexing -- Image Ranking -- Image Presentation -- Appendix: Deriving the Conditional Probability of a Gaussian Process.

This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining Emphasizes how to deal with real image data for practical image mining Highlights how such features as color, texture, and shape can be mined or extracted from images for image representation Presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees Discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods Provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter This easy-to-follow work illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing. Dr. Dengsheng Zhang is a Senior Lecturer in the School of Science, Engineering and Information Technology at Federation University Australia.

ISBN: 9783030179892$q(electronic bk.)

Standard No.: 10.1007/978-3-030-17989-2doiSubjects--Topical Terms:

790766
Multimedia data mining.

LC Class. No.: QA76.9.D343 / Z43 2019

Dewey Class. No.: 006.312

Fundamentals of image data mininganalysis, features, classification and retrieval /
LDR:03561nmm a2200349 a 4500 001 558691
003 DE-He213
005 20190513070726.0
006 m d
007 cr nn 008maaau
008 191219s2019 gw s 0 eng d
020 $a 9783030179892$q(electronic bk.)
020 $a 9783030179885$q(paper)
024 7 $a 10.1007/978-3-030-17989-2 $2 doi
035 $a 978-3-030-17989-2
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343 $b Z43 2019
072 7 $a UYT $2 bicssc
072 7 $a COM012000 $2 bisacsh
072 7 $a UYT $2 thema
072 7 $a UYQV $2 thema
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b Z63 2019
100 1 $a Zhang, Dengsheng. $3 841410
245 1 0 $a Fundamentals of image data mining $h [electronic resource] : $b analysis, features, classification and retrieval / $c by Dengsheng Zhang.
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2019.
300 $a xxxi, 314 p. : $b ill., digital ; $c 24 cm.
490 1 $a Texts in computer science, $x 1868-0941
505 0 $a Part I: Preliminaries -- Fourier Transform -- Windowed Fourier Transform -- Wavelet Transform -- Part II: Image Representation and Feature Extraction -- Color Feature Extraction -- Texture Feature Extraction -- Shape Representation -- Part III: Image Classification and Annotation -- Bayesian Classification -- Support Vector Machines -- Artificial Neural Networks -- Image Annotation with Decision Trees -- Part IV: Image Retrieval and Presentation -- Image Indexing -- Image Ranking -- Image Presentation -- Appendix: Deriving the Conditional Probability of a Gaussian Process.
520 $a This reader-friendly textbook presents a comprehensive review of the essentials of image data mining, and the latest cutting-edge techniques used in the field. The coverage spans all aspects of image analysis and understanding, offering deep insights into areas of feature extraction, machine learning, and image retrieval. The theoretical coverage is supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments. Topics and features: Describes the essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms Reviews a varied range of state-of-the-art models, algorithms, and procedures for image mining Emphasizes how to deal with real image data for practical image mining Highlights how such features as color, texture, and shape can be mined or extracted from images for image representation Presents four powerful approaches for classifying image data, namely, Bayesian classification, Support Vector Machines, Neural Networks, and Decision Trees Discusses techniques for indexing, image ranking, and image presentation, along with image database visualization methods Provides self-test exercises with instructions or Matlab code, as well as review summaries at the end of each chapter This easy-to-follow work illuminates how concepts from fundamental and advanced mathematics can be applied to solve a broad range of image data mining problems encountered by students and researchers of computer science. Students of mathematics and other scientific disciplines will also benefit from the applications and solutions described in the text, together with the hands-on exercises that enable the reader to gain first-hand experience of computing. Dr. Dengsheng Zhang is a Senior Lecturer in the School of Science, Engineering and Information Technology at Federation University Australia.
650 0 $a Multimedia data mining. $3 790766
650 1 4 $a Image Processing and Computer Vision. $3 274051
650 2 4 $a Data Mining and Knowledge Discovery. $3 275288
650 2 4 $a Machine Learning. $3 833608
650 2 4 $a Engineering Mathematics. $3 806481
650 2 4 $a Big Data. $3 760530
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Texts in computer science. $3 559643
856 4 0 $u https://doi.org/10.1007/978-3-030-17989-2
950 $a Computer Science (Springer-11645)