Information Fusion : Machine Learning Methods / by Jinxing Li, Bob Zhang, David Zhang.

Format:

Book

Author/Creator:

Li, Jinxing, Author.

Zhang, Bob, Author.

Zhang, David., Author.

Contributor:

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Data mining.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Data Mining and Knowledge Discovery.

Physical Description:

1 online resource (XXVI, 260 pages) : 1 illustrations

Edition:

1st ed. 2022.

Contained In:

Springer Nature eBook

Place of Publication:

Singapore : Springer Nature Singapore : Imprint: Springer, 2022.

System Details:

text file PDF

Summary:

In the big data era, increasing information can be extracted from the same source object or scene. For instance, a person can be verified based on their fingerprint, palm print, or iris information, and a given image can be represented by various types of features, including its texture, color, shape, et cetera These multiple types of data extracted from a single object are called multi-view, multi-modal or multi-feature data. Many works have demonstrated that the utilization of all available information at multiple abstraction levels (measurements, features, decisions) helps to obtain more complex, reliable and accurate information and to maximize performance in a range of applications. This book provides an overview of information fusion technologies, state-of-the-art techniques and their applications. It covers a variety of essential information fusion methods based on different techniques, including sparse/collaborative representation, kernel strategy, Bayesian models, metric learning, weight/classifier methods, and deep learning. The typical applications of these proposed fusion approaches are also presented, including image classification, domain adaptation, disease detection, image restoration, et cetera This book will benefit all researchers, professionals and graduate students in the fields of computer vision, pattern recognition, biometrics applications, et cetera Furthermore, it offers a valuable resource for interdisciplinary research.

Contents:

Chapter 1. Introduction

Chapter 2. Information fusion based on sparse/collaborative representation

Chapter 3. Information fusion based on gaussian process latent variable model

Chapter 4. Information fusion based on multi-view and multifeature earning

Chapter 5. Information fusion based on metric learning

Chapter 6. Information fusion based on score/weight classifier fusion

Chapter 7. Information fusion based on deep learning

Chapter 8. Conclusion.

Other Format:

Printed edition:

ISBN:

978-981-16-8976-5

9789811689765

Access Restriction:

Restricted for use by site license.