Transcript Application of Machine Learning to Color Matching

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
1) Color matching: its importance
2) Our Approach: the judge machine
3) Application: automatic color adviser
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
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
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
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
2.2 Machine Learning
▌
A computer can learn general knowledge
from training data
 Not
much number of training data are needed
Unknown
data
Good
examples
Bad
examples
2. Our proposal : Judge machine
Good!
Knowledge
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
2.3 Prototype experiment
▌
Prepared data :
 Classified
good pairs and bad pairs by hand
 200 examples
▌
Trained with the first 100 pairs
Good
Pairs
Prepared
Data
Bad
Pairs
2. Our proposal : Judge machine
Used for training
Used for Evaluation
2.3 Prototype experiment (cont.)
▌
Compared the judge of the computer and
teacher (human)
▌ Accuracy was 80%
>> Learning was successful!
Computer Teacher
Goo
d
Goo
d
Goo
d
Bad
2. Our proposal : Judge machine
3. Automatic Color Adviser
▌
Generate infinite good color pairs
 Generate
random color pairs
 Select pairs that passed the judgment
Generate pairs randomly
3. Automatic Color Adviser
Judge
machine
Good pairs are selected
3. Automatic Color Adviser (cont.)
▌
Practical applications :
 Website
design
 PowerPoint presentations
 Word documents
▌
There are no similar function for now
3. Automatic Color Adviser
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)
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/