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Advanced Sport Image Classification Using Spatial Color and Posture Context Descriptors

This research aims to improve image searching by accurately identifying sports in images through the use of complex background descriptors, posture context features, and neural classifiers. Various techniques such as majority color extraction, DCT transformation, RGB to YCbCr conversion, and SVD decomposition are employed for feature extraction. Experimental results on a dataset of 300 images with six sports show promising classification accuracy compared to existing methods.

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Advanced Sport Image Classification Using Spatial Color and Posture Context Descriptors

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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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