Bag-of-Features for Image Classification - Lecture 17

Explore the concept of Bag-of-Features (Bag-of-Words) in computer vision through this 47-minute lecture from the University of Central Florida's 2012 Computer Vision course. Delve into image classification techniques, feature distribution, texture elements, and the use of visual words. Learn about dense features, clustering methods like K-means algorithm, and classification approaches including support vector machines. Understand the importance of linear and nonlinear boundaries in image recognition, and gain insights into the Pascal Competition and evaluation matrices. Presented by Dr. Mubarak Shah, this lecture provides a comprehensive overview of Bag-of-Features methodology and its applications in computer vision.

BagofFeatures
Contents
Image Classification
Distribution of Features
Texture Elements
Words
Big Up Words
Image Recognition
Dense Features
Clustering
Kmeans
Algorithm
Visual Words
Classification
Margin
Support vectors
LibSVM
Linear and nonlinear boundaries
Pascal Competition
Evaluation Matrix