Use the deepness to view image representation.

1. Lu, Wang-Chou
Image Representation
Usage Guide 2014/10/01 @ 林⼜⼝口

2. Why Image Representation?
Machine Learning Course @ Caltech
Xi = 500 x 375 D
Low Dimensional Vector

3. Map of Representation
Robust
Color Histogram, !
Computation Cost
PCA, !
Sparse Coding, !
Bag of Visual Word, !
DPM, !
Deep Learning…

4. Outline
❖ Hand Crafted Features!
❖ Machine Learning approach!
❖ Hierarchical Approaches!
❖ When to use? Real-time vs Precision

5. Hand Crafted Features
❖ Color Histogram, Template, Haar Features!
❖ Interested Point Detector + HOG!
❖ Bag of Visual Word

6. Simple Features
Histogram Based Haar Features
Template Based

7. SIFT Like Approach
HOG, 3780D, !
overlapped 7 x 15 cells * !
SIFT 128D,|V| = 1! ( normalized 2x2 grid) * 9 bins
4 X 4 grid * 8 bins
David Lowe [IJCV 2004] N Dalal et al [CVPR2005]

8. Bag of Visual Word + SPM
SVM
SIFT!
Descriptor
S. Lazebnik et al [cvpr06]

9. Machine Learning Approach
❖ Dimensionality Reduction!
❖ PCA, Manifold Learning, Sparse Coding, LSH!
❖ Deformable Part Model!
❖ Neural Network!
❖ Convolution Neural Network

10. Principle Component Analysis
MA Turk et al [cvpr91]

11. Manifold Learning
[ISOMAP, LLE 2003]

12. Sparse Coding
reconstruction error sparsity
Y: Input Vector!
B: Basis Matrix!
Z: weight
H Lee et al. [NIPS 2007]

13. Locality Sensitive Hashing Embedding

14. Deformable Part Model
Pedro F. Felzenszwalb et al [PAMI 2010]

15. Neural Network
Tanh & Sigmoid !
nonlinear function

16. Convolution Neural Network
LeCun 1989
Krizhevsky et al. [NIPS2012]
ReLu

17. State of the Art
GoogleNet 2014
MSRA2014

18. Deepness Table
Convolution Neural Network & Deformable Part Model use max pooling, !
others use sum pooling or say histogram pooling

19. Image Representation Usage Guide
Deepness
5
4.5
4
3.5
3
2.5
2
1.5
1
Real Time Application
Interactive Application
Convolution Neural Network
BoW+ SPM, Deformable Part Model
Bag of Visual Word
SIFT/HOG
Color Histogram
iPhone 5s Tegra K1 or PC Geforce Titan HPC
45 gflops 370 gflops 5.1 tera flops
gflops for Single Precision, PC: i7 3.5G 4 cores parellel computing
TRAINING TIME NOT INCLUDED

20. Some Tips
❖ GPU ~= 50 CPU Cores!
❖ Hand Crafted Feature is shallow, higher feature
template need to be learnt.!
❖ Do Dimensionality Reduction!
❖ Deeper Features, More Training Data!
❖ Handle Invariance: Registration vs Spatial Pooling!
❖ The Learnt Deep Representation(CNN) is shareable