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P.Panakarn*, S.Phimoitares, and C.Lursinsap

COMPLEX SPORT IMAGE CLASSIFICATION USING SPATIAL COLOR and POSTURE CONTEXT DESCRIPTORS and NEURAL CLASSIFIERS. P.Panakarn*, S.Phimoitares, and C.Lursinsap Advanced Virtual and Intelligent Computing ( AV IC) Research Center Department of Mathematics,C hulalongkorn University, Bangkok,Thailand.

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P.Panakarn*, S.Phimoitares, and C.Lursinsap

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  1. COMPLEX SPORT IMAGE CLASSIFICATION USING SPATIAL COLORand POSTURE CONTEXT DESCRIPTORS and NEURAL CLASSIFIERS P.Panakarn*, S.Phimoitares, and C.Lursinsap Advanced Virtual and Intelligent Computing ( AV IC) Research Center Department of Mathematics,C hulalongkorn University, Bangkok,Thailand

  2. GOAL • Improvement of image searching. • To know what sport is in the image. • Find features for good classification accuracy. • More descriptive of the postures.

  3. outline • Other features • Majority color extraction(color histogram) • Descriptor for complex background(DCT) • Posture descriptor(Cb,Cr) • SVD on DCT,Cb,Cr • Experiment

  4. Other features • Edge histogram • Region-based shape • EH & RS will compare with the feasure proposed later.

  5. Majority color extraction • RGB color 64bin colors • Use the most significant two bits of each color channel. • Make histogram

  6. Majority color extraction

  7. Descriptor for complex background • Change from color domain to frequency domain • Discrete cosine transform • RGBgrayDCT

  8. Descriptor for complex background

  9. Posture descriptor • RGB YCbCr • No Y because it is too sensitive to colors

  10. SVD on DCT,Cb,Cr • The three matrices , DCT,Cb,Cr is the image size. • The essential information must be extracted. • Diagonal elements will be used.

  11. SVD on DCT,Cb,Cr • For DCT,Cb,Cr matrices • SVD(single valued decomposition)

  12. Experiment • 300 images with 6 sports each • Baseball, Basketball, Field and Track Skiing, Soccer, and Swimming • 200 for training , 100 for testing • 130*200 pixels

  13. Experiment • The elements after SVD is 130 for DCT,Cb,Cr matrices • Features are 130*3+64=454 features • Compare with (EH & RS) using NNC,RBF

  14. Experiment

  15. Experiment

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