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Art Authentication & Painting Style Classification

Art Authentication & Painting Style Classification Wan-Ting Lee Chin-Sheng Chen 5/10/2006 Multimedia Security Systems Outline Authentication Author Identification Van Gogh Style Classification Light Line Color Texture Impressionism Fauvism Cubism Discuss Experiment Result

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Art Authentication & Painting Style Classification

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  1. Art Authentication &Painting Style Classification Wan-Ting Lee Chin-Sheng Chen 5/10/2006 Multimedia Security Systems

  2. Outline • Authentication • Author Identification • Van Gogh • Style Classification • Light • Line • Color • Texture • Impressionism • Fauvism • Cubism • Discuss Experiment Result • References

  3. Authentication • Paintings

  4. Authentication • Architecture

  5. Authentication • DWT

  6. Authentication • Feature Vectors

  7. Authentication • Feature Vectors

  8. Authentication • Hausdorff Distance 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 1 0 2.5138 3.1485 2.2648 3.2077 0.3649 3.6279 2.8836 3.1286 4.8374 1.1268 1.7501 4.1207 1.9475 4.7728 1.2088 3.3849 2.0807 2 2.5138 0 0.7593 1.3405 0.9127 2.4660 1.2342 0.9155 0.8748 7.1452 2.2211 3.7787 6.5029 2.8895 7.0592 3.0610 5.7533 2.4759 3 3.1485 0.7593 0 1.3854 0.8367 3.0766 0.7638 0.7775 0.5699 7.3764 2.8485 3.6503 6.4038 2.7290 7.4651 2.9318 5.6631 2.3055 4 2.2648 1.3405 1.3854 0 1.4814 2.3037 1.8908 1.3111 1.5514 6.3835 2.3156 3.0889 5.5681 1.8293 6.5477 2.0869 4.8392 1.8396 5 3.2077 0.9127 0.8367 1.4814 0 3.1528 0.5555 0.9025 0.8777 7.4384 3.0608 4.0441 6.5157 2.4781 7.5707 3.0329 5.7923 2.5825 6 0.3649 2.4660 3.0766 2.3037 3.1528 0 3.5856 2.8271 3.0443 4.6922 0.9045 1.5982 4.0534 2.0226 4.6101 1.2382 3.2996 1.9540 7 3.6279 1.2342 0.7638 1.8908 0.5555 3.5856 0 1.2034 0.8294 7.8813 3.4433 4.2974 6.9231 2.9301 8.0038 3.4231 6.1903 2.8199 8 2.8836 0.9155 0.7775 1.3111 0.9025 2.8271 1.2034 0 0.8796 7.1276 2.7115 3.1676 5.7300 2.0364 7.2455 2.2491 4.9904 2.0579 9 3.1286 0.8748 0.5699 1.5514 0.8777 3.0443 0.8294 0.8796 07.3492 2.7663 3.7328 6.4338 2.5272 7.4234 2.9315 5.7061 2.1347 104.8374 7.1452 7.3764 6.3835 7.4384 4.6922 7.8813 7.1276 7.34920 5.1007 4.9358 4.2166 5.8919 0.8869 5.3750 3.9531 5.9221 11 1.1268 2.2211 2.8485 2.3156 3.0608 0.9045 3.4433 2.7115 2.7663 5.1007 0 1.8544 4.5788 2.2423 4.9970 1.5414 3.8230 1.7300 121.7501 3.7787 3.6503 3.0889 4.0441 1.5982 4.2974 3.1676 3.7328 4.9358 1.8544 0 3.3527 3.0613 4.9231 1.8555 2.5260 1.9664 134.1207 6.5029 6.4038 5.5681 6.5157 4.0534 6.9231 5.7300 6.4338 4.2166 4.5788 3.3527 0 4.6408 4.4928 4.0566 3.1402 4.5774 141.9475 2.8895 2.7290 1.8293 2.4781 2.0226 2.9301 2.0364 2.5272 5.8919 2.2423 3.0613 4.6408 0 6.0952 1.6531 4.0130 2.0245 154.7728 7.0592 7.4651 6.5477 7.5707 4.6101 8.0038 7.2455 7.4234 0.8869 4.9970 4.9231 4.4928 6.0952 0 5.5506 4.1029 5.8931 161.2088 3.0610 2.9318 2.0869 3.0329 1.2382 3.4231 2.2491 2.9315 5.3750 1.5414 1.8555 4.0566 1.6531 5.5506 0 2.8359 1.7943 173.3849 5.7533 5.6631 4.8392 5.7923 3.2996 6.1903 4.9904 5.7061 3.9531 3.8230 2.5260 3.1402 4.0130 4.1029 2.8359 0 3.8305 182.0807 2.4759 2.3055 1.8396 2.5825 1.9540 2.8199 2.0579 2.1347 5.9221 1.7300 1.9664 4.5774 2.0245 5.8931 1.7943 3.8305 0

  9. Authentication • MDS • Distance3D: Without BP • True Paintings 0.2428 0.1589 0.1482 0.2285 0.2064 0.2163 0.1422 0.0441 0.2737 • Imitation Paintings 0.7019 0.3713 0.6394 0.8643 0.5679 0.6853 0.2347 0.4119 0.7159 • Distance3D : With Block Process • True Paintings 0.2350 0.2426 0.0883 0.1471 0.1942 0.2460 0.1552 0.2014 0.0729 • Imitation Paintings 0.7421 0.3266 0.5909 0.8896 0.4663 0.7425 0.3506 0.7251 0.2686

  10. Authentication • Without Block Processing

  11. Authentication • With Block Processing

  12. Outline • Authentication • Author Identification • Vangogh • Style Classification • Light • Line • Color • Texture • Impressionism • Fauvism • Cubism • Discussion Experiment Result • References

  13. Style Classification: Light • Six different light features • P1: Percentage of dark colors • P2: Gradient coefficient • P3: Standard Deviation of Mean

  14. Style Classification: Light (cont.) • Six different light features • P4: Number of local and global maxima in luminance histogram • P5: Peak point of luminance histogram correspond • P6: Skew value

  15. Style Classification: Light (cont.) Painting style V.S. grayscale histogram Fauvism Cubism Impressionism

  16. Outline • Authentication • Author Identification • Vangogh • Style Classification • Light • Line • Color • Texture • Impressionism • Fauvism • Cubism • Discussion Experiment Result • References

  17. Style Classification: Line • Number of lines • Do the edge detection • Added up the number of lines that were 8 pixels in length or longer across the edge. • Get the number (number of line).

  18. Style Classification: Line (cont.) Impressionism=0.0460 Fauvism=0.0032 Cubism=0.0996

  19. Style Classification: Line (cont.) • Experiment result Use Sobel Filter, Threshold=.25

  20. Outline • Authentication • Author Identification • Vangogh • Style Classification • Light • Line • Color • Texture • Impressionism • Fauvism • Cubism • Discussion Experiment Result • References

  21. Style Classification: Color • RGBXY • Characterize the spatial distribution of colors • Paintings with larger palette scopes and larger variations in spatial color distribution will have larger singular values • HS histogram • Number of colors • Divided into six colors bins (red, yellow, green, cyan, blue, and magenta),

  22. Style Classification: Color (cont.) Impressionism Fauvism Cubism

  23. Style Classification

  24. Outline • Authentication • Author Identification • Vangogh • Style Classification • Light • Line • Color • Texture • Impressionism • Fauvism • Cubism • Discussion Experiment Result • References

  25. Style Classification: Texture • Why using Gabor Filter? Cubism Impressionism Fauvism

  26. Style Classification: Texture • Gabor Filter & Feature Vectors

  27. Style Classification: Texture • Simulation Result

  28. Style Classification • Any better approach to improve the accuracy? • W1, W2, and W3 are the weighting values

  29. Improvement Result Style Classification

  30. Discuss Experiment Result • Resolution Problems • Image Sizes • How to solve the different styles in the sphere boundary?

  31. References • [1] S. Lyu, D. Rockmore and H. Farid, “A digital technique for art authentication,” PNAS, Dec. 2004 • [2] Robert W. B., and Eero P. S., “ Image compression via joint statistical characterization in the wavelet domain,” IEEE Trans. on Image Processing, Vol. 8, No. 12, Dec. 1999 • [3] Siwei Lyu and Hany Farid, “Detecting Hidden Messages Using Higher-Order Statistics and Support Vector Machines,” 5th International Workshop on Information Hiding, Noordwijkerhout. • [4] B. S. Manjunath and W. Y. Ma, “Texture Features for Browsing and Retrieval of Image Data,” IEEE Trans on Pattern Analysis Machine Intelligence, Vol. 18, NO. 8, August 1996. • [5] Anil K. Jain and Farshid Farrokhnia, “ Unsupervised Texture Segmentation Using Gabor Filters,” IEEE 1990. • [6] Daniel P. H., Gregory A. K., and William J. R., “Comparing Images Using the Hausdorff Distance,” IEEE Trans on Pattern Analysis and Machine Intelligence, Vol. 15, No. 9, Sep. 1993 • [7] Thomas Lombardi, “The Classification of Style in Fine-Art Painting,” CSIS, Pace University, May. 2005 • [8] Oguz Icoglu, Bilge Gunsel, and Sanem Sariel, “Classification and Indexing of Paintings Based on Art Movement” , Multimedia Signal Processing and Pattern Recognition Lab. • [9 ] Florin Cutzu, Riad Hammoud, Alex Leykin, “Estimating the photorealism of images: Distinguishing paintings from photographs”, Department of Computer Science, Indiana University. • [10] Greg Pass Ramin ZabihJustin Miller, “Comparing Images Using Color Coherence Vectors”, Computer Science Department Cornell University • [11] N.poich. The Web Museum. http://www.ibiblio.org/wm/paint/ • [12] The Museum of Modern Art. http://www.moma.org • [13] The Metropolitan Museum of Art. http://www.metmuseum.org

  32. Thank You

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