1. Learning Mid-Level Features For Recognition
Y-Lan Boureau, Francis Bach, Yann LeCun and Jean Ponce
Published in CVPR 2010
Presented by Bo Chen, 8.20,2010

2. Outline 1. Classification System 2. Brief introduction of each step
3. Systematic evaluation of unsupervised mid-level features
4. Learning discriminative dictionaries
5. Average and max pooling
6. Conclusions

3. System Flow Chart Patches
SIFT
Coding
Pooling
Classifier
SPM
Subsampling sliding patches to cover all of details
Robust features invariant to some conditions
Generate mid-level feature, such as sparse coding,
vector quantization and deep network
Max-pooling or average pooling
Spatial pyramid model
Linear or nonlinear SVM

4. Scale Invariant Feature Transform (D. Lowe, IJCV,2004)
Motivations: Image matching, scale invariance, rotation invariance,
illumination invariance and viewpoint invariance
Figures from David Lee’s ppt

5. Calculate SIFT Descriptors
Divide a 16x16 patch into 4 subregions, 8 bins in each subregion
which leads to a 4x4x8=128 dimensional vector. (low-level)
Figures from Jason Clemons’s ppt

6. Notations Question: How can we represent each region?
Figure from S. Lazebnik et.al, CVPR06

7. Coding and Pooling Vector quantization (Bag-of-features) Sparse Coding

8. Systematic evaluation of unsupervised mid-level features

9. Macrofeatures and Denser SIFT Sampling
Parameterizations:
SIFT sampling density
macrofeature side length
subsampling parameter
Results:
Caltech101: 75.7% (4, 2, 4)
Scene: 84.3% (8, 2, 1)

10. Results

11. Discriminative Dictionaries
Algorithm: stochastic gradient descent
Cons: high computational complexity
Solutions:
1.approximate z(n) by pooling over a random sample of ten locations of the image.
2. Update only a random subset of coordinates at each iteration.
Scenes dataset

12. Average and Max Pooling
Why pooling?
Pooling is used to achieve invariance to image transformations,
more compact representations, and better robustness to noise and
clutter so as to preserves important information while discarding
irrelevant detail, the crux of the matter being to determine what falls
in which category.
Max-pooling vs. Average-pooling
The authors show that using max pooling on hard vector quantized
features in a spatial pyramid brings the performance of linear
classification to the level of that obtained by Lazebnik et al. (2006)
with an intersection kernel, even though the resulting feature is
binary.
Our feeling
Pooling helps the learned codes sparse, which follows the human
visual function. Especially, for convolutional deep network, pooling
appears very necessary since there is correlations between neighbor
contents.
Part conclusion from Y-Lan Boureau et. al, ICML 2010

13. Theoretical Comparison of Average and Max Pooling
Experimental methodology: Binary classification (positive and negative)

14. Conclusions
1. Give a comprehensive and systematic comparison across each step of mid-level feature extraction through several types of coding modules (hard and soft vector quantization, sparse coding) and pooling schemes (by taking the average, or the maximum), which obtains state-of-the-art performance or better on several recognition benchmarks.
2. Supervised dictionary learning method for sparse coding
3. Theoretical and empirical insight into the remarkable performance of max pooling.