Skip to main content
SpringerLink
Log in

Anthropocentric Semantic Information Extraction from Movies

  • Chapter
Computational Intelligence in Multimedia Processing: Recent Advances

Part of the book series: Studies in Computational Intelligence ((SCI,volume 96))

In this chapter we will describe new methods for anthropocentric semantic video analysis, and will concentrate our efforts to provide a uniform framework by which media analysis can be rendered more useful for retrieval applications as well as for human–computer interaction based application. The main idea behind anthropocentric video analysis is that a film is to be viewed as an artwork and not as a mere of frames following each others. We will show that this kind of analysis which is a straightforward approach of human perception of a movie can finally produce some interesting results of the overall annotation of a video content. “Anthropos” which is the greek word for “human” show the intent of our proposition to concentrate in humans in a movie. Humans are the most essential part of a movie and thus we track down all important features that we can get from low-level and mid-level feature algorithms such as face detection, face tracking, eye detection, visual speech recognition, 3D face reconstruction, face clustering, face verification and facial expressions extraction. All these algorithms produce results which are stored in an MPEG-7 inspired description scheme set which implements the way humans are connecting those features. Therefore as a results we have a structured information of all features that can be found for a specific human (e.g. actor). As it will be shown in this chapter this approach as a straightforward approach of human perception provides a new way of media analysis in the semantic level.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 329.00
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. M. -H. Yang, D. J. Kriegman, and N. Ahuja, Detecting faces in images: A survey, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 1, pp. 34–58, 2002.

    Article  Google Scholar 

  2. E. Hjelmas and B. K. Low, Face detection: A survey, Computer Vision and Image Understanding, vol. 83, pp. 236–274, 2001.

    Article  MATH  Google Scholar 

  3. G. Welch and E. Foxlin, Motion tracking: No silver bullet, but a respectable arsenal, IEEE Computer Graphics and Applications, special issue on “Tracking”, vol. 22, no. 6, pp. 24–38, November/December 2002.

    Article  Google Scholar 

  4. T. B. Moeslund and E. Granum, A survey of computer vision-based human motion capture, Computer Vision and Image Understanding, vol. 81, pp. 231–268, 2001.

    Article  MATH  Google Scholar 

  5. D. M. Gavrila, The visual analysis of human movement: A survey, Computer Vision and Image Understanding, vol. 73, no. 1, pp. 82–98, 1999.

    Article  MATH  Google Scholar 

  6. G. Chow and L. Xiaobo, Towards a system for automatic facial feature detection, Pattern Recognition, vol. 26, no. 12, pp. 1739–1755, 1993.

    Article  Google Scholar 

  7. G. Feng and P. Yuen, Multi-cues eye detection on gray intensity image, Pattern Recognition, vol. 34, no. 5, pp. 1033–1046, 2001.

    Article  MATH  Google Scholar 

  8. K. LAM and H. YAN, Locating and extracting the eye in human face images, Pattern recognition, vol. 29, no. 5, pp. 771–779, 1996.

    Article  Google Scholar 

  9. T. Chen and R. Rao, Audio-visual integration in multimodal communication, Proceedings of the IEEE, vol. 86, no. 5, pp. 837–852, 1998.

    Article  Google Scholar 

  10. M. J. R., Visual Speech Recognition with Stochastic Networks, Proceedings of the IEEE, vol. 86, no. 5, pp. 837–852, 1998.

    Google Scholar 

  11. E. Trucco and A. Verri, Introductory Techniques for 3-D Computer Vision. Prentice Hall PTR Upper Saddle River, NJ, USA, 1998.

    Google Scholar 

  12. M. Pollefeys, ‘Tutorial on 3D modelling from figures, http://www.esat.kuleuven.ac.be/∼pollefey/tutorial/, June 2000.

  13. N. Vretos, V. Solachidis, and I. Pitas, A mutual information based face clustering algorithm for movies, Multimedia and Expo, 2006 IEEE International Conference on, pp. 1013–1016, 2006.

    Google Scholar 

  14. O. Arandjelovic and A. Zisserman, Automatic face recognition for film character retrieval in feature-length films, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, San Diego, 2005, pp. 860–867.

    Google Scholar 

  15. A. Fitzgibbon and A. Zisserman, On affine invariant clustering and automatic cast listing in movies, in: ECCV, 2002.

    Google Scholar 

  16. T. L. Berg, A. C. Berg, J. Edwards, M. Maire, R. White, Y. W. Teh, E. Learned-Miller, and D. A. Forsyth, Names and faces in the news, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, (CVPR’04), vol. 2nd. IEEE, 2004, pp. 848–854.

    Google Scholar 

  17. J. Matas, M. Hamou, K. Jonsson, J. Kittler, Y. Li, C. Kotropoulos, A. Tefas, I. Pitas, T. Tan, H. Yan, F. Smeraldi, J. Bigun, N. Capdevielle, W. Gerstner, S. Ben-Yacouba, Y. Abdelaoued, and E. Mayoraz, Comparison of face verification results on the xm2vts database, in: Proc. of 2000 Int. Conf. on Pattern Recognition (ICPR’00), 2000, pp. 858–863.

    Google Scholar 

  18. K. Messer, J. Kittler, M. Sadeghi, S. Marcel, C. Marcel, S. Bengio, F. Cardinaux, C. Sanderson, J. Czyz, L. Vandendorpe, S. Srisuk, M. Petrou, W. Kurutach, A. Kadyrov, R. Paredes, B. Kepenekci, F. Tek, G. Akar, F. Deravi, and N. Mavity, Face verification competition on the xm2vts database, in: AVBPA03, 2003, pp. 964–974.

    Google Scholar 

  19. L. Juwei, K. Plataniotis, and A. Venetsanopoulos, Face recognition using lda-based algorithms, IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 195–200, 2003.

    Article  Google Scholar 

  20. L. Juwei, Face recognition using kernel direct discriminant analysis algorithms, IEEE Transactions on Neural Networks, vol. 14, no. 1, pp. 117–126, 2003.

    Article  Google Scholar 

  21. P. Ekman and W. V. Friesen, Emotion in the Human Face. Prentice Hall, New Jersey, 1975.

    Google Scholar 

  22. T. Kanade, J. Cohn, and Y. Tian, Comprehensive database for facial expression analysis, in: Proceedings of IEEE International Conference on Face and Gesture Recognition, March 2000, pp. 46–53.

    Google Scholar 

  23. M. Pantic and L. Rothkrantz, Expert system for automatic analysis of facial expressions, Image and Vision Computing, vol. 18, no. 11, pp. 881–905, 2000.

    Article  Google Scholar 

  24. K. Sobottka and I. Pitas, Looking for faces and facial features in color images, Pattern Recognition and Image Analysis: Advances in Mathematical Theory and Applications, Russian Academy of Sciences, vol. 7, no. 1, pp. 124–137, 1997.

    Google Scholar 

  25. R. Lienhart and J. Maydt, An extended set of Haar-like features for rapid object detection, Image Processing. 2002. Proceedings. 2002 International Conference on, vol. 1, 2002.

    Google Scholar 

  26. J. Shi and C. Tomasi, Good features to track. in: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR94), Seattle, United States, June 1994, pp. 593–600.

    Google Scholar 

  27. B. D. Zarit, B. J. Super, and F. K. H. Quek, Comparison of five color models in skin pixel classification, in: ICCV99 International Workshop on Recognition, Analysis, and Tracking of Faces and Gestures in Real-Time Systems (RATFG-RTS99), Corfu, Greece, September 1999, pp. 58–63.

    Google Scholar 

  28. B. Martinkauppi, M. Soriano, and M. Laaksonen, Behavior of skin color under varying illumination seen by different cameras in different color spaces, in Machine Vision Applications in Industrial Inspection IX, Martin Hunt, Editor Proceedings of SPIE, vol. 4301, Coimbra, Portugal, July 1999, pp. 102–112.

    Google Scholar 

  29. V. Vezhnevets, V. Sazonov, and A. Andreeva, A survey on pixel-based skin color detection techniques, in: International Conference on Computer Graphics Between Europe and Asia (GRAPHICON-2003), Moscow, Russia, September 2003.

    Google Scholar 

  30. A. Fitzgibbon and R. Fisher, A buyer’s guide to conic fitting, in: Fifth British Machine Vision Conference (BMVC99), Birmingham, UK, 1995, pp. 513–522.

    Google Scholar 

  31. E. Loutas, K. Diamantaras, and I. Pitas, Occlusion resistant object tracking, in: IEEE International Conference on Image Processing (ICIP01), vol. 2, Thessaloniki, Greece, October 2001, pp. 65–68.

    Google Scholar 

  32. Z. Zhou and X. Geng, Projection functions for eye detection, Pattern Recognition, vol. 37, no. 5, pp. 1049–1056, 2004.

    Article  MATH  Google Scholar 

  33. J. Wu and Z. Zhou, Efficient face candidates selector for face detection, Pattern Recognition, vol. 36, no. 5, pp. 1175–1186, 2003.

    Article  Google Scholar 

  34. O. Jesorsky, K. Kirchberg, R. Frischholz, et al., Robust face detection using the hausdorff distance, Proceedings of Audio and Video Based Person Authentication, pp. 90–95, 2001.

    Google Scholar 

  35. W. Rucklidge, Efficient Visual Recognition Using the Hausdorff Distance. Springer, 1996.

    Google Scholar 

  36. D. Cristinacce, T. Cootes, and I. Scott, A multi-stage approach to facial feature detection, 15th British Machine Vision Conference, London, England, pp. 277–286, 2004.

    Google Scholar 

  37. K. Messer, J. Matas, J. Kittler, J. Luettin, and G. Maitre, XM2VTSDB: The Extended M2VTS Database, Second International Conference on Audio and Video-based Biometric Person Authentication, vol. 626, 1999.

    Google Scholar 

  38. The bioid face database.

    Google Scholar 

  39. M. Turk and A. Pentland, Face recognition using eigenfaces, Computer Vision and Pattern Recognition, 1991. Proceedings CVPR’91., IEEE Computer Society Conference on, pp. 586–591, 1991.

    Google Scholar 

  40. J. Canny, A computational approach to edge detection, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 8, no. 6, pp. 679–698, 1986.

    Article  Google Scholar 

  41. A. MacLeod and Q. Summerfield, A procedure for measuring auditory and audio-visual speech-reception thresholds for sentences in noise: rationale, evaluation, and recommendations for use. British Journal of Audiology, vol. 24, no. 1, pp. 29–43, 1990.

    Article  Google Scholar 

  42. J. Luettin, N. Thacker, and S. Beet, Speechreading using shape and intensity information, Proceedings of the Fourth IEEE International Conference on Spoken Language Processing, vol. 1, pp. 58–61, 1996.

    Article  Google Scholar 

  43. P. de Cuetos, C. Neti, and A. Senior, Audio-visual intent-to-speak detection for human–computer interaction, ICASSP IEEE INT CONF ACOUST SPEECH SIGNAL PROCESS PROC, vol. 4, pp. 2373–2376, 2000.

    Google Scholar 

  44. M. Siracusa, L. Morency, K. Wilson, J. Fisher, and T. Darrell, A multi-modal approach for determining speaker location and focus, Proceedings of the Fifth International Conference on Multimodal interfaces, pp. 77–80, 2003.

    Google Scholar 

  45. S. Siatras, N. Nikolaidis, and I. Pitas, Visual speech detection using mouth region intensities, in Proceedings of European Signal Processing Conference (EUSIPCO 2006), September 2006.

    Google Scholar 

  46. P. Viola and M. Jones, Robust Real-Time Face Detection, International Journal of Computer Vision, vol. 57, no. 2, pp. 137–154, 2004.

    Article  Google Scholar 

  47. P. Viola and M. Jones, Rapid object detection using a boosted cascade of simple features, Proceedings of IEEE CVPR, vol. 1, pp. 511–518, 2001.

    Google Scholar 

  48. S. Asteriadis, N. Nikolaidis, and I. Pitas, An Eye Detection Algorithm Using Pixel to Edge Information, in: Proceedings of ISCCSP 2006, vol. 1, 2006.

    Google Scholar 

  49. S. Kay, Fundamentals of Statistical Signal Processing, Volume 2: Detection Theory. Prentice Hall PTR, 1998.

    Google Scholar 

  50. R. Hartley and A. Zisserman, Multiple View Geometry in Computer Vision. Cambridge University Press, 2003.

    Google Scholar 

  51. O. Faugeras, What can be seen in three dimensions with an uncalibrated stereo rig, Proceedings of the Second European Conference on Computer Vision, pp. 563–578, 1992.

    Google Scholar 

  52. P. Beardsley, A. Zisserman, and D. Murray, Sequential Updating of Projective and Affine Structure from Motion, International Journal of Computer Vision, vol. 23, no. 3, pp. 235–259, 1997.

    Article  Google Scholar 

  53. R. Hartley, Euclidean reconstruction from uncalibrated views, Applications of Invariance in Computer Vision, vol. 825, pp. 237–256, 1994.

    Google Scholar 

  54. M. Rydfalk, CANDIDE: A parameterized face, Linkoping University, Tech. Rep., 1978.

    Google Scholar 

  55. B. Triggs, P. McLauchlan, R. Hartley, and A. Fitzgibbon, Bundle Adjustment – A modern synthesis, Vision Algorithms: Theory and Practice, vol. 1883, pp. 298–372, 2000.

    Article  Google Scholar 

  56. M. Everingham and A. Zisserman, Automated person identification in video. in CIVR, 2004, pp. 289–298.

    Google Scholar 

  57. Z. He, X. Xu, and S. Deng, K-anmi: A mutual information based clustering algorithm for categorical data, 2005. [Online]. Available: http://www.citebase.org/cgi-bin/citations?id=oai:arXiv.org:cs/0511013

  58. R. L. Cannon, J. V. Dave, and J. C. Bezdek, Efficient implementation of the fuzzy c-means clustering algorithms, IEEE Trans. Pattern Anal. Mach. Intell., vol. 8, no. 2, pp. 248–255, 1986.

    Article  MATH  Google Scholar 

  59. M. Turk and A. P. Pentland, Eigenfaces for recognition. Journal of Cognitive Neuroscience, vol. 3, no. 1, pp. 71–86, 1991.

    Article  Google Scholar 

  60. P. N. Belhumeur, J. P. Hespanha, and D. J. Kriegman, Eigenfaces vs. fisherfaces: Recognition using class specific linear projection. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 711–720, July 1997.

    Article  Google Scholar 

  61. M. Lades, J. C. Vorbrüggen, J. Buhmann, J. Lange, C. von der Malsburg, R. P. Würtz, and W. Konen, Distortion invariant object recognition in the dynamic link architecture. IEEE Transactions on Computers, vol. 42, no. 3, pp. 300–311, Mar. 1993.

    Article  Google Scholar 

  62. B. Duc, S. Fischer, and J. Bigün, Face authentication with Gabor information on deformable graphs. IEEE Transactions on Image Processing, vol. 8, no. 4, pp. 504–516, 1999.

    Article  Google Scholar 

  63. C. Kotropoulos, A. Tefas, and I. Pitas, Frontal face authentication using discriminating grids with morphological feature vectors. IEEE Transactions on Multimedia, vol. 2, no. 1, pp. 14–26, Mar. 2000.

    Article  Google Scholar 

  64. M. Kirby and L. Sirovich, Application of the Karhunen-Loeve procedure for the characterization of human faces. IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 12, no. 1, pp. 103–108, Jan. 1990.

    Article  Google Scholar 

  65. D. L. Swets and J. Weng, Using discriminant eigenfeatures for image retrieval, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 8, pp. 831–836, 1996. [Online]. Available: citeseer.ist.psu.edu/swets96using.html

  66. A. Martinez and A. Kak, Pca versus lda,IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 23, no. 2, pp. 228–233, 2001.

    Google Scholar 

  67. L. Wiskott, J. Fellous, N. Krüger, and C. von der Malsburg, Face recognition by elastic bunch graph matching. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775–779, 1997.

    Article  Google Scholar 

  68. A. Tefas, C. Kotropoulos, and I. Pitas, Using support vector machines to enhance the performance of elastic graph matching for frontal face authentication, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 23, no. 7, pp. 735–746, 2001.

    Article  Google Scholar 

  69. P. T. Jackway and M. Deriche, Scale-space properties of the multiscale morphological dilation-erosion, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 18, no. 1, pp. 38–51, 1996. [Online]. Available: citeseer.ist.psu.edu/jackway92scale.html

  70. I. Pitas and A. Venetsanopoulos, Nonlinear Digital Filters: Principles and Applications. Norwell, MA: Kluwer, Academic Publishers, 1990.

    MATH  Google Scholar 

  71. B. Fasel and J. Luettin, Automatic facial expression analysis: A survey, Pattern Recognition, vol. 36, no. 1, pp. 259–275, 2003.

    Article  MATH  Google Scholar 

  72. I. Cohen, N. Sebe, S. Garg, L. S. Chen, and T. S. Huanga, Facial expression recognition from video sequences: temporal and static modelling, Computer Vision and Image Understanding, vol. 91, pp. 160–187, 2003.

    Article  Google Scholar 

  73. Y. Zhang and Q. Ji, Active and dynamic information fusion for facial expression understanding from image sequences, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 5, pp. 699–714, May 2005.

    Article  Google Scholar 

  74. M. S. Bartlett, G. Littlewort, I. Fasel, and J. R. Movellan, Real time face detection and facial expression recognition: Development and applications to human computer interaction, in: Proceedings of Conference on Computer Vision and Pattern Recognition Workshop, vol. 5, Madison, Wisconsin, 16–22 June 2003, pp. 53–58.

    Google Scholar 

  75. M. J. Lyons, S. Akamatsu, M. Kamachi, and J. Gyoba, Coding facial expressions with Gabor wavelets, in: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, 1998, pp. 200–205.

    Google Scholar 

  76. M. J. Lyons, J. Budynek, and S. Akamatsu, Automatic classification of single facial images, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 21, no. 12, pp. 1357–1362, 1999.

    Article  Google Scholar 

  77. L. Wiskott, J. Fellous, N. Kruger, and C. v. d. Malsburg, Face recognition by elastic bunch graph matching, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 775–779, July 1997.

    Article  Google Scholar 

  78. G. Guo and C. R. Dyer, Learning from examples in the small sample case: Face expression recognition, IEEE Transactions on Systems, Man, and Cybernetics-Part B: Cybernetics, vol. 35, no. 3, pp. 477–488, June 2005.

    Article  Google Scholar 

  79. Z. Zhang, M. Lyons, M. Schuster, and S. Akamatsu, Comparison between geometry-based and Gabor-wavelets-based facial expression recognition using multi-layer perceptron, in: Proceedings of the Third IEEE International Conference on Automatic Face and Gesture Recognition, Nara Japan, 14–16 April 1998, pp. 454–459.

    Google Scholar 

  80. B. Fasel, Multiscale facial expression recognition using convolutional neural networks, IDIAP, Tech. Rep., 2002.

    Google Scholar 

  81. M. Matsugu, K. Mori, Y. Mitari, and Y. Kaneda, Subject independent facial expression recognition with robust face detection using a convolutional neural network, Neural Networks, vol. 16, no. 5–6, pp. 555–559, June–July 2003.

    Article  Google Scholar 

  82. M. Rosenblum, Y. Yacoob, and L. S. Davis, Human expression recognition from motion using a radial basis function network architecture, IEEE Transactions on Neural Networks, vol. 7, no. 5, pp. 1121–1138, September 1996.

    Article  Google Scholar 

  83. L. Ma and K. Khorasani, Facial expression recognition using constructive feedforward neural networks, IEEE Transactions on Systems, Man, And Cybernetics-Part B: Cybernetics, vol. 34, no. 3, pp. 1588–1595, June 2004.

    Article  Google Scholar 

  84. S. Dubuisson, F. Davoine, and M. Masson, A solution for facial expression representation and recognition, Signal Processing: Image Communication, vol. 17, no. 9, pp. 657–673, October 2002.

    Article  Google Scholar 

  85. X.-W. Chen and T. Huang, Facial expression recognition: A clustering-based approach, Pattern Recognition Letters, vol. 24, no. 9–10, pp. 1295–1302, June 2003.

    Article  MATH  Google Scholar 

  86. Y. Gao, M. Leung, S. Hui, and M. Tananda, Facial expression recognition from line-based caricatures, IEEE Transactions on Systems, Man and Cybernetics-Part A: Systems and Humans, vol. 33, no. 3, pp. 407–412, May 2003.

    Article  Google Scholar 

  87. B. Abboud, F. Davoine, and M. Dang, Facial expression recognition and synthesis based on an appearance model, Signal Processing: Image Communication, vol. 19, no. 8, pp. 723–740, 2004.

    Article  Google Scholar 

  88. I. A. Essa and A. P. Pentland, Facial expression recognition using a dynamic model and motion energy, in: Proceedings of the International Conference on Computer Vision (ICCV 95), Cambridge, MA, 20–23 June 1995.

    Google Scholar 

  89. M. Pantic and L. J. M. Rothkrantz, Automatic analysis of facial expressions: The state of the art, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1424–1445, December 2000.

    Article  Google Scholar 

  90. I. A. Essa and A. P. Pentland, Coding, analysis, interpretation, and recognition of facial expressions, IEEE Transactions Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 757–763, July 1997.

    Article  Google Scholar 

  91. M. S. Bartlett, G. Littlewort, B. Braathen, T. J. Sejnowski, and J. R. Movellan, An approach to automatic analysis of spontaneous facial expressions, in: Proceedings of Fifth IEEE International Conference on Automatic Face and Gesture Recognition (FGR’02), Washington, D.C., 2002.

    Google Scholar 

  92. G. Donato, M. S. Bartlett, J. C. Hager, P. Ekman, and T. J. Sejnowski, Classifying Facial Actions, IEEE Trans. on Pattern Analysis and Machine Intelligence, vol. 21, no. 10, pp. 974–989, 1999.

    Article  Google Scholar 

  93. Y. L. Tian, T. Kanade, and J. Cohn, Recognizing Facial Actions by combining geometric features and regional appearance patterns, Robotics Institute, Carnegie Mellon University, Tech. Rep. CMU-RI-TR-01-01, 2001.

    Google Scholar 

  94. J. J. Lien, T. Kanade, J. Cohn, and C. C. Li, Automated facial expression recognition based on FACS Action Units, in: Proceedings of Third IEEE International Conference on Automatic Face and Gesture Recognition, April 1998, pp. 390–395.

    Google Scholar 

  95. J. J. Lien, T. Kanade, J. F. Cohn, and C. Li, Detection, tracking, and classification of Action Units in facial expression, Journal of Robotics and Autonomous Systems, July 1999.

    Google Scholar 

  96. Y. L. Tian, T. Kanade, and J. Cohn, Evaluation of Gabor wavelet-based Facial Action Unit recognition in image sequences of increasing complexity, in: Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition, 2002, pp. 229–234.

    Google Scholar 

  97. A. Tefas, C. Kotropoulos, and I. Pitas, Using Support Vector Machines for face authentication based on elastic graph matching, in: Proceedings of the IEEE International Conference Image Processing (ICIP’2000), 2000, pp. 29–32.

    Google Scholar 

  98. H. Drucker, W. Donghui, and V. Vapnik, Support vector machines for spam categorization, IEEE Transactions on Neural Networks, vol. 10, no. 5, pp. 1048–1054, September 1999.

    Article  Google Scholar 

  99. A. Ganapathiraju, J. Hamaker, and J. Picone, Applications of support vector machines to speech recognition, IEEE Transactions on Signal Processing, vol. 52, no. 8, pp. 2348–2355, August 2004.

    Article  Google Scholar 

  100. M. Pontil and A. Verri, Support vector machines for 3D object recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, no. 6, pp. 637–646, 1998.

    Article  Google Scholar 

  101. I. Kotsia and I. Pitas, Facial expression recognition in image sequences using geometric deformation features and support vector machines, IEEE Transactions on Image Processing, vol. 16, no. 1, pp. 172–187, January 2007.

    Article  Google Scholar 

  102. S. Zafeiriou, A. Tefas, I. Buciu, and I. Pitas, Exploiting discriminant information in non-negative matrix factorization with application to frontal face verification, IEEE Transactions on Neural Networks, vol. 17, no. 3, pp. 683–695, May 2006.

    Article  Google Scholar 

  103. I. Kotsia and I. Pitas, Real time facial expression recognition from image sequences using support vector machines, in: IEEE International Conference on Image Processing (ICIP), 11–14 September 2005, pp. 966–969.

    Google Scholar 

  104. V. Vapnik, Statistical learning theory. Wiley, New York, 1998.

    MATH  Google Scholar 

  105. R. Chellappa, C. L. Wilson, and S. Sirohey, Human and machine recognition of faces: A survey. Proceedings of the IEEE, vol. 83, no. 5, pp. 705–740, May 1995.

    Article  Google Scholar 

  106. J. P. Eakins, Retrieval of still images by content, Lectures on information retrieval, pp. 111–138, 2001.

    Google Scholar 

  107. J. K. Aggarwal and Q. Cai, Human motion analysis: A review, Computer Vision and Image Understanding, vol. 73, no. 3, pp. 428–440, 1999.

    Article  Google Scholar 

  108. E. Sikudova, M. A. Gavrielides, and I. Pitas, Extracting semantic information from art images, in: Proceedings of International Conference on Computer Vision and Graphics 2004 (ICCVG 2004), Warsaw, Poland, 22–24 September 2004.

    Google Scholar 

  109. M. Krinidis, G. Stamou, H. Teutsch, S. Spors, N. Nikolaidis, R. Rabenstein, and I. Pitas, An audio-visual database for evaluating person tracking algorithms, in: Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, Philadelphia, USA, 18–23 March 2005, pp. 452–455.

    Google Scholar 

  110. ISO (International Organization for Standardization), Overview of the MPEG-7 standard, International Organization for Standardization, Geneva, Switzerland, ISO Standard ISO/IEC JTC1/SC29 N4509, Dec. 2001.

    Google Scholar 

  111. G. Ahanger and T. D. C. Little, Data semantics for improving retrieval performance of digital news video systems, IEEE Transactions on Knowledge and Data Engineering, vol. 13, no. 3, pp. 352–360, 2001.

    Article  Google Scholar 

  112. M. Kyperountas, Z. Cernekova, C. Kotropoulos, M. Gavrielides, and I. Pitas, Scene change detection using audiovisual clues, in: Proceedings of Norwegian Conference on Image Processing and Pattern Recognition (NOBIM 2004), Stavanger, Norway, 27–28 May 2004.

    Google Scholar 

  113. Z. Cernekova, I. Pitas, and C. Nikou, Information theory-based shot cut/fade detection and video summarization, IEEE Transactions on Circuits and Systems for Video Technology, vol. 16, no. 1, pp. 82–91, January 2006.

    Article  Google Scholar 

  114. ISO (International Organization for Standardization), Information technology– multimedia content description interface - part 5: Multimedia description schemes, International Organization for Standardization, Geneva, Switzerland, ISO Standard ISO/IEC JTC 1/SC 29 N 4161, Dec. 2001.

    Google Scholar 

  115. N. N. G. Stamou and I. Pitas, Object tracking based on morphological elastic graph matching, in Proceedings of the IEEE International Conference on Image Processing (ICIP 2005), Genova, Italy, September 2005.

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Informatics, Aristotle University of Thessaloniki, 451, 54124, Thessaloniki, Greece

    Nicholas Vretos, Vassilios Solachidis & Ioannis Pitas

Authors
  1. Nicholas Vretos
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Vassilios Solachidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ioannis Pitas
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Quantitative and Information System Department, Kuwait University College of Business Administration, 5486, 13055, Kuwait

    Aboul-Ella Hassanien

  2. Centre for Quantifiable Quality of Service in Communication Systems (Q2S) Centre of Excellence, Norwegian University of Science and Technology, O.S. Bragstads plass 2E, 7491, Trondheim, Norway

    Ajith Abraham

  3. Systems Research Institute, Polish Academy of Sciences, Newelska 6, 01-447, Warsaw

    Janusz Kacprzyk

Rights and permissions

Reprints and permissions

Copyright information

© 2008 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Vretos, N., Solachidis, V., Pitas, I. (2008). Anthropocentric Semantic Information Extraction from Movies. In: Hassanien, AE., Abraham, A., Kacprzyk, J. (eds) Computational Intelligence in Multimedia Processing: Recent Advances. Studies in Computational Intelligence, vol 96. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-76827-2_17

Download citation

  • DOI: https://doi.org/10.1007/978-3-540-76827-2_17

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-76826-5

  • Online ISBN: 978-3-540-76827-2

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation