Contextual Image Classification: Understanding Visual Data for Effective Classification
この電子書籍について
A method of classification that is based on the contextual information contained in images is referred to as contextual image classification. This method falls under the category of pattern recognition in computer vision. A "contextual" approach is one that focuses on the relationship between the pixels that are in close proximity to one another, which is also referred to as the neighborhood. The classification of the photographs by the utilization of the contextual information is the objective of this approach.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Contextual image classification
Chapter 2: Pattern recognition
Chapter 3: Gaussian process
Chapter 4: LPBoost
Chapter 5: One-shot learning (computer vision)
Chapter 6: Least-squares support vector machine
Chapter 7: Fraunhofer diffraction equation
Chapter 8: Symmetry in quantum mechanics
Chapter 9: Bayesian hierarchical modeling
Chapter 10: Paden-Kahan subproblems
(II) Answering the public top questions about contextual image classification.
(III) Real world examples for the usage of contextual image classification in many fields.
Who this book is for
Professionals, undergraduate and graduate students, enthusiasts, hobbyists, and those who want to go beyond basic knowledge or information for any kind of Contextual Image Classification.
