By: Jared Meyer

A Neural Network Approach to Classifying

Cartoons Based on Color

ECE 539 Final Project

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

• 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%

Project Steps• Ripped 3-5 episodes of following shows:

Avatar: The Last Airbender The Real Ghost BustersBatman RebootCourage the Cowardly Dog Samurai JackCowboy Bebop The SimpsonsEd, Edd, n’ Eddy South ParkFamily Guy SpidermanFuturama Spongebob SquarepantsInvader Zim SupermanOutlaw Star Teenage Mutant Ninja TurtlesPowerpuff Girls Teen Titans

These form the 20 outputs for ANN

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

Data Calculations

• Wrote program in C# to calculate 14 Features per image

• Brightness, Contrast, Saturation, RGB ratio• ‘Lininess’

– Pixels with large brightness difference

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

Results

• Data varied greatly, even in same episode– ~5% classification rates

• Averaged 10 random frames together

Results

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

Results• Much better classification rates with new data

Final Results: 5 Features: Brightness, Saturation, RGBANN Structure: 2 Hidden layers, 9 neurons

Classification Rates: 57.14% on Training 47.50% on Testing

Pretty good, considering we had 20 classes.