Explore the Bag of Words model and its applications in image classification through this comprehensive lecture. Delve into the origins of bag-of-features in texture recognition and text analysis, and learn how to apply this concept to visual data. Discover the process of feature extraction, including dense features, and understand the importance of quantization using K-means clustering. Examine various datasets such as PASCAL 2007, CalTech 101, and action recognition datasets like UCF Sports and UCF YouTube. Gain insights into spatial pyramid matching, scene classification, and retrieval examples. Investigate advanced techniques like Histogram of Optical Flow (HOF) and its implementation steps. Master the fundamentals of image representation and classification using the Bag of Visual Words model, equipping yourself with essential knowledge for computer vision and machine learning applications.
Overview
Syllabus
Intro
Difficulties: within class variations
Bag-of-features - Origin: texture recognition
Bag of Words Model
Bag-of-features - Origin: bag-of-words (text)
Bag-of-features for image classification
feature extraction
Dense features
Step 2: Quantization
K-means Clustering: Step 1 Algorithm: kmeans, Distance Metric Euclidean Distance
Example: 3-means Clustering
Examples for visual words
Training data Vectors are histograms, one from each training image
Examples for misclassified images
Evaluation of image classification
PASCAL 2007 dataset
Results for PASCAL 2007
Step 3: Classification
Image representation
Spatial pyramid matching
Spatial pyramid representation
Scene classification
Retrieval examples
Category classification - CalTech 101
Discussion
Weizmann Action Dataset
KTH Data Set
UCF Sports Action Dataset
IXMAS Multi-view Data Set
UCF YouTube Action Dataset (UCF-11)
Bag of Visual Words model (II)
Histogram of Optical flow (HOF)
HOF Steps
Taught by
UCF CRCV
