1. Dog/Cat Classifier
Christina Stiff

2. Motivation Web accessibility Classic dog/cat example Screen readers
Automatic image classification
Classic dog/cat example
Motivation
Brainstorming > job > web accessibility
Manual: time-consuming, miss images
Get my feet wet in deep learning

3. Approach MATLAB Convolutional Neural Network (CNN)
Neural Network Toolbox
Convolutional Neural Network (CNN)
Learns features directly
MATLAB
CNN
Deeper layers learn higher level features

4. Data Kaggle Dog and Cat Dataset 12,500 dogs 12,500 cats
Non-uniform size
Grayscale
File errors
Obstructed view of animal

5. Image Pre-Processing ZCA whitening (Krizhevsky 2009)
Make input less redundant
For input images:
Less correlated features
Same variance for all the features

6. Image Pre-Processing Difference of Gaussians
First part of SIFT, a feature extraction algorithm
Image Pre-Processing
Experiment
CS 534 – Computational Photography
If network learns distinct features, why not input images with edges emphasized
DoG
Apply Gaussian kernel to an image
Subtraction of one blurred image from another
Increase visibility of edges

7. Results Trial 1 Trial 2 Sample: 2,000 dogs/cats Dimensions: 50x50x3
No pre-processing
Layers: 1-5-1
Accuracy: 59.4%
Runtime: 6m
Sample: 12,500 dogs/cats
Dimensions: 50x50x3
No pre-processing
Layers: 1-5-1
Accuracy: 62.1%
Runtime: 39m
Started out playing with network parameters
First crack:
Only 10% better than random classification
Room for improvement
Second trial:
Increased total sample size by factor of 3
Conclusion: increasing training data does not make a huge difference
Runtime does not include image pre-processing

8. Results Trial 16 Trial 22 Sample: 12,500 dogs/cats Dimensions: 64x64x3
No pre-processing
Layers:
Accuracy: 79.2%
Runtime: 26m
Sample: 3,000 dogs/cats
Dimensions: 64x64x3
Pre-processing: ZCA
Layers:
Accuracy: 74.4%
Runtime: 22m
Biggest difference between Trial 2 and Trial 22 is the number of layers – 5 vs. 10
Trial 22, with ZCA whitening, got comparable results in accuracy and time to Trial 16 (but with ¼ the amount of images)

9. Results Trial 26 Sample: 3,000 dogs/cats Dimensions: 64x64x3
Pre-processing: SIFT
Layers:
Accuracy: 74.6%
Runtime: 1h 2m
Biggest difference between Trial 2 and Trial 22 is the number of layers – 5 vs. 10
Trial 22, with ZCA whitening, got comparable results in accuracy and time to Trial 16 (but with ¼ the amount of images)

10. Discussion Kaggle Dog/Cat competition: 98.94% Room for improvement!
Comparison
Winner: Pierre Sermanet
GPU memory limitation– low resolution
Improvement
Image manipulations: rotation, reflection, contrast
Network parameters – Dropout layer, learning rate

11. https://www.mathworks.com/discovery/convolutional-neural- network.html
big-data-on-gpus-and-in-parallel.html
References