A Neural Network Approach to Classifying Cartoons Based on Color
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By: Jared Meyer A Neural Network Approach to Classifying Cartoons Based on Color ECE 539 Final Project
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A Neural Network Approach to Classifying Cartoons Based on Color. ECE 539 Final Project. By: Jared Meyer. 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 - PowerPoint PPT Presentation
Transcript of A Neural Network Approach to Classifying Cartoons Based on Color
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.
