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This useful textbook/reference presents an accessible primer on the fundamentals of image texture analysis, as well as an introduction to the K-views model for extracting and classifying image textures. Divided into three parts, the book opens with a review of existing models and algorithms for image texture analysis, before delving into the details of the K-views model. The work then concludes with a discussion of popular deep learning methods for image texture analysis.

Topics and features: provides self-test exercises in every chapter; describes the basics of image texture, texture features, and image texture classification and segmentation; examines a selection of widely-used methods for measuring and extracting texture features, and various algorithms for texture classification; explains the concepts of dimensionality reduction and sparse representation; discusses view-based approaches to classifying images; introduces the template for the K-views algorithm, as well as a range of variants of this algorithm; reviews several neural network models for deep machine learning, and presents a specific focus on convolutional neural networks.

This introductory text on image texture analysis is ideally suitable for senior undergraduate and first-year graduate students of computer science, who will benefit from the numerous clarifying examples provided throughout the work.

Image Texture Analysis: Foundations, Models and Algorithms

Part I: Existing Models and Algorithms for Image Texture

Image Texture, Texture Features, and Image Texture Classification and Segmentation

Texture Features and Image Texture Models

Algorithms for Image Texture Classification

Dimensionality Reduction and Sparse Representation

Part II: The K-Views Models and Algorithms

Basic Concept and Models of the K-Views

Using Datagram in the K-Views Model

Features-Based K-Views Model

Advanced K-Views Algorithms

Part III: Deep Machine Learning Models for Image Texture Analysis

Foundations of Deep Machine Learning in Neural Networks

Convolutional Neural Networks and Texture Classification