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Fundamentals of image data mining = ...

Zhang, Dengsheng.

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Fundamentals of image data mining = analysis, features, classification and retrieval /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of image data mining/ by 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.
[NT 15003449]:	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.
Contained By:	Springer eBooks
Subject:	Multimedia data mining. -
Online resource:	https://doi.org/10.1007/978-3-030-17989-2
ISBN:	9783030179892

Fundamentals of image data mining = analysis, 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

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

3251475
Multimedia data mining.

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

Dewey Class. No.: 006.312

Fundamentals of image data mining = analysis, features, classification and retrieval /
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245 1 0 $a Fundamentals of image data mining $h [electronic resource] : $b analysis, features, classification and retrieval / $c by Dengsheng Zhang.
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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.
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