Fundamentals of Image Data Mining: Analysis, Features, Classification and Retrieval

This textbook provides a comprehensive review of image data mining, covering essential tools, exercises, and state-of-the-art models. It integrates mathematics and computer science to solve image mining problems and demonstrates how features like color, texture, and shape can be mined for image representation. The book is suitable for students and researchers in computer science and mathematics.

Format: Paperback / softback
Length: 363 pages
Publication date: 26 June 2022
Publisher: Springer Nature Switzerland AG

This comprehensive and valuable textbook provides a thorough examination of the fundamentals and cutting-edge techniques in image data mining. It encompasses a wide range of topics, including feature extraction, machine learning, and image retrieval, offering deep insights into these areas. The theoretical foundations are supported by practical mathematical models and algorithms, utilizing data from real-world examples and experiments.

Topics and Features:

The textbook introduces essential tools for image mining, covering Fourier transforms, Gabor filters, and contemporary wavelet transforms. It develops numerous new exercises, most with MATLAB code and instructions, to facilitate hands-on learning.

Each chapter concludes with review summaries, aiding in consolidating the key concepts learned. The book also analyzes state-of-the-art models, algorithms, and procedures for image mining, highlighting their applications in various domains.

New sections have been incorporated on pre-processing, discrete cosine transform, and statistical inference and testing, providing comprehensive coverage of image data mining techniques.

The book demonstrates how features such as color, texture, and shape can be mined or extracted for image representation. Powerful classification approaches, including Bayesian classification, support vector machines, neural networks, and decision trees, are implemented and applied to solve image data mining problems.

Imaging techniques for indexing, ranking, and presentation, as well as database visualization, are also discussed, enabling the integration of image data with other data sources.

This textbook is designed to be accessible to students and researchers in computer science, mathematics, and other scientific disciplines. It offers practical applications and solutions that can be applied in real-world scenarios. The hands-on exercises provide opportunities for the reader to gain practical experience in computing and apply the learned concepts.

In summary, this textbook is an essential resource for anyone interested in image data mining, providing a comprehensive and up-to-date coverage of the field. It serves as a valuable guide for students, researchers, and practitioners seeking to advance their knowledge and expertise in this rapidly evolving area.

Weight: 611g
Dimension: 235 x 155 (mm)
ISBN-13: 9783030692537
Edition number: 2nd ed. 2021