What is Bag of Words Model
In computer vision, the bag-of-words model sometimes called bag-of-visual-words model can be applied to image classification or retrieval, by treating image features as words. In document classification, a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary. In computer vision, a bag of visual words is a vector of occurrence counts of a vocabulary of local image features.
How you will benefit
(I) Insights, and validations about the following topics:
Chapter 1: Bag-of-words model in computer vision
Chapter 2: Image segmentation
Chapter 3: Scale-invariant feature transform
Chapter 4: Scale space
Chapter 5: Automatic image annotation
Chapter 6: Structure from motion
Chapter 7: Sub-pixel resolution
Chapter 8: Mean shift
Chapter 9: Articulated body pose estimation
Chapter 10: Part-based models
(II) Answering the public top questions about bag of words model.
(III) Real world examples for the usage of bag of words model 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 Bag of Words Model.
