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Automatic Color Matching with a Computer

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.

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Automatic Color Matching with a Computer

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  1. Automatic Color Matching with a Computer Kei Takahashi Dept. of Computer Science and Engineering Helsinki University of Technology

  2. How do you like these color pairs? Universally accepted pairs

  3. How about these? Controversial pairs (Should be avoided)

  4. Computerized Color Matching • Color matching: its importance • Our Approach: the judge machine • Application: automatic color adviser

  5. 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

  6. 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

  7. 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

  8. 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

  9. 2.2 Machine Learning • A computer can learn general knowledge from training data • Not much number of training data are needed Good examples Unknown data Good! Bad examples Knowledge 2. Our proposal : Judge machine

  10. 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

  11. 2.3 Prototype experiment • Prepared data : • Classified good pairs and bad pairs by hand • 200 examples • Trained with the first 100 pairs Prepared Data Good Pairs Used for training Used for Evaluation Bad Pairs 2. Our proposal : Judge machine

  12. 2.3 Prototype experiment (cont.) • Compared the judge of the computer and teacher (human) • Accuracy was 80% >> Learning was successful! Computer Teacher Good Good Good Bad 2. Our proposal : Judge machine

  13. 3. Automatic Color Adviser • Generate infinite good color pairs • Generate random color pairs • Select pairs that passed the judgment Judge machine Good pairs are selected Generate pairs randomly 3. Automatic Color Adviser

  14. 3. Automatic Color Adviser (cont.) • Practical applications : • Website design • PowerPoint presentations • Word documents • There are no similar function for now 3. Automatic Color Adviser

  15. 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)

  16. 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/

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