automatic color matching with a computer
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Automatic Color Matching with a Computer. Kei Takahashi Dept. of Computer Science and Engineering Helsinki University of Technology. How do you like these color pairs?. Universally accepted pairs. How about these?. Controversial pairs (Should be avoided). Computerized Color Matching. - PowerPoint PPT PresentationTRANSCRIPT
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Automatic Color Matching with a Computer
Kei Takahashi
Dept. of Computer Science and Engineering
Helsinki University of Technology
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How do you like these color pairs?
Universally accepted pairs
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How about these?
Controversial pairs(Should be avoided)
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Computerized Color Matching
1) Color matching: its importance
2) Our Approach: the judge machine
3) Application: automatic color adviser
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1.1 Color matching: its importance
▌ Judge if two (or more) colors match or not▌ Depend on persons and situations▌ Still there are universally accepted pairs
1. Color matching: its importance
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1.2 Commercial importance▌ Coloring makes all the difference
It changes customer’s impression
▌ Colorings improve/diminish the value Should avoid ”controversial pairs”
▌ Needs to be unique and impressive Always new color designs are needed
1. Color matching: its importance
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1.3 Growths of demands▌ PC program offers infinite colors▌ Everyone need to design
PowerPoint, Word, websites…
▌ Always unique design is needed Color design is a tough work Can’t afford to put out to
professional designers
1. Color matching: its importance
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2.1 The judge machine▌ We designed a program which judges if a
color pair is good or bad
Good!
Bad!
2. Our proposal : Judge machine
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2.2 Machine Learning▌ A computer can learn general knowledge
from training data Not much number of training data are needed
Badexamples
Goodexamples
Unknowndata
2. Our proposal : Judge machine
Good!Knowledge
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2.2 Machine Learning (cont.)▌ Prepare training data
Evaluated by “teacher” Not so many data is needed (ex.100)
▌ Train a computer with the data The computer acquires general knowledge
▌ Now it can judge any color pairs Acts just as the teacher
2. Our proposal : Judge machine
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2.3 Prototype experiment▌ Prepared data :
Classified good pairs and bad pairs by hand 200 examples
▌ Trained with the first 100 pairs
PreparedData
Used for training
Used for Evaluation
GoodPairs
BadPairs
2. Our proposal : Judge machine
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2.3 Prototype experiment (cont.)
▌ Compared the judge of the computer and teacher (human)
▌ Accuracy was 80% >> Learning was successful!
Computer Teacher
Good Good
BadGood
2. Our proposal : Judge machine
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3. Automatic Color Adviser▌ Generate infinite good color pairs
Generate random color pairs Select pairs that passed the judgment
Generate pairs randomly Good pairs are selected
3. Automatic Color Adviser
Judgemachine
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3. Automatic Color Adviser (cont.)
▌ Practical applications : Website design PowerPoint presentations Word documents
▌ There are no similar function for now
3. Automatic Color Adviser
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Conclusion▌ Color matching is an important area▌ Judge machine divides good/bad color pairs▌ Infinite number of good color pairs are
obtained in no time(Automatic Color Adviser)
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Further Information▌ Our website:
http://www.sodan.ecc.u-tokyo.ac.jp/~kei/cl/
▌ SVM (machine learning) http://www.csie.ntu.edu.tw/~cjlin/libsvm/