Fundamentals of Image Data Mining : Analysis, Features, Classification and Retrieval / by Dengsheng Zhang.

Format:

Book

Author/Creator:

Zhang, Dengsheng, author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Texts in computer science 1868-0941

Texts in Computer Science, 1868-0941

Language:

English

Subjects (All):

Optical data processing.

Data mining.

Machine learning.

Engineering mathematics.

Big data.

Image Processing and Computer Vision.

Data Mining and Knowledge Discovery.

Machine Learning.

Engineering Mathematics.

Big Data.

Local Subjects:

Image Processing and Computer Vision.

Data Mining and Knowledge Discovery.

Machine Learning.

Engineering Mathematics.

Big Data.

Physical Description:

1 online resource (XXXI, 314 pages) : 202 illustrations, 117 illustrations in color.

Edition:

First edition 2019.

Contained In:

Springer eBooks

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2019.

System Details:

text file PDF

Summary:

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.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-030-17989-2

9783030179892

9783030179885

9783030179908

9783030179915

Access Restriction:

Restricted for use by site license.