1. By: Jared Meyer A Neural Network Approach to Classifying Cartoons Based on Color ECE 539 Final Project

2. Project Plans Collect Data – Choose 20 different animated series – Choose 3-5 episodes at random Varying seasons if applicable – Covert each episode to series of images Calculate data for each image – Write program in C# Build Artificial Neural Network with bp.m – Find structure/data combination that maximize classification rate

3. Why? – Big fan of cartoons/animated series – Interested in how images are represented in computers – Would be neat to see color patterns in shows Existing Results – Weather classification based on color (Moosmann, 2008) – Linear kernel Support Vector Machine – 3 classes Clear Light Rain Heavy Rain – Average classification rate: 89%

4. Project Steps Ripped 3-5 episodes of following shows: Avatar: The Last AirbenderThe Real Ghost Busters BatmanReboot Courage the Cowardly DogSamurai Jack Cowboy BebopThe Simpsons Ed, Edd, n’ EddySouth Park Family GuySpiderman FuturamaSpongebob Squarepants Invader ZimSuperman Outlaw StarTeenage Mutant Ninja Turtles Powerpuff GirlsTeen Titans  These form the 20 outputs for ANN

5. Project Steps Converted episodes to images – X Video Converter – One BMP image per 200 frames Remove first frame – Usually pure black Remove all frames including end credits – Would add bias

6. Data Calculations Wrote program in C# to calculate 14 Features per image Brightness, Contrast, Saturation, RGB ratio ‘Lininess’ – Pixels with large brightness difference

7. Data Calculations ‘Important Areas’ – Pixels brighter than average brightness Counted Red, Orange, Yellow, Green, Blue, Violet, Grey Pixels in ‘Important Areas’ Finally, used bp.m program to build ANN using back-propagation algorithm

8. Results Data varied greatly, even in same episode – ~5% classification rates Averaged 10 random frames together

9. Results Contrast, Color counts still varied too much – Removed them; didn’t show much pattern

10. Results Much better classification rates with new data Final Results: 5 Features: Brightness, Saturation, RGB ANN Structure: 2 Hidden layers, 9 neurons Classification Rates: 57.14% on Training 47.50% on Testing Pretty good, considering we had 20 classes.