1. Multiple Feature Learning for Action Classification
Goal: Classify actions in images
Approach
SIFT, HOG, LBP, and color histogram features
Use dense samping, LLC, and SPM to get
histogram intersection kernel for each feature
Compared stacking, feature selection, and
randomization techniques to kernel learning
Combine kernels using multiple kernel learning:
Riding horse K1 K2
Kfinal
C-SVM Km
Multiple Kernel Learning
From Wang et al., 2010
Ben Poole (Computer Science Department, Stanford University) 1

2. Multiple Feature Learning for Action Classification
Combining features improves performance by 10%
Stacking feature vectors yields similar performance to MKL
Randomly selected max features (WTA-Hash) also achieve similar performance for a smaller computational cost
Ben Poole (Computer Science Department, Stanford University) 2