This Useful Textbookreference 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.