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Bottom-up spatiotemporal visual attention model for video analysis

Bottom-up spatiotemporal visual attention model for video analysis

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© The Institution of Engineering and Technology
Published

The human visual system (HVS) has the ability to fixate quickly on the most informative (salient) regions of a scene and therefore reducing the inherent visual uncertainty. Computational visual attention (VA) schemes have been proposed to account for this important characteristic of the HVS. A video analysis framework based on a spatiotemporal VA model is presented. A novel scheme has been proposed for generating saliency in video sequences by taking into account both the spatial extent and dynamic evolution of regions. To achieve this goal, a common, image-oriented computational model of saliency-based visual attention is extended to handle spatiotemporal analysis of video in a volumetric framework. The main claim is that attention acts as an efficient preprocessing step to obtain a compact representation of the visual content in the form of salient events/objects. The model has been implemented, and qualitative as well as quantitative examples illustrating its performance are shown.

Inspec keywords: image sequences; video signal processing

Other keywords: visual content; bottom-up spatiotemporal visual attention model; regions dynamic evolution; video sequences; video analysis; visual uncertainty; image-oriented computational model; saliency-based visual attention

Subjects: Optical, image and video signal processing; Video signal processing

References

    1. 1)
    2. 2)
      • C.R. Bolles , H.H. Baker . Generalizing epipolar-plane image analysis on the spatio-temporal surface. Int. J. Comput. Vis. , 33 - 49
    3. 3)
      • S. Baluja , D.A. Pomerleau . Expectation-based selective attention for the visual monitoring and control of a robot vehicle. Robot. Auton. Syst. , 329 - 344
    4. 4)
      • Martins, F.C., Ding, W., Feig, E.: `Joint control of spatial quantization and temporal sampling for very low bit rate video', Proc. ICASSP, May 1997, p. 2072–2075.
    5. 5)
    6. 6)
      • N. Moenne-Loccoz , E. Bruno , S. Marchand-Maillet . Knowledge-based detection of events in video streams from salient regions of activity. Pattern Anal. Appl. , 4 , 422 - 429
    7. 7)
      • C. Koch , S. Ullman . Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. , 219 - 227
    8. 8)
    9. 9)
    10. 10)
    11. 11)
    12. 12)
      • Rapantzikos, K., Tsapatsoulis, N.: `Enhancing the robustness of skin-based face detection schemes through a visual attention architecture', Proc. of the IEEE International Conf. on Image Processing (ICIP), September 2005, Genova, Italy.
    13. 13)
      • P.K. Kaiser , R.M. Boynton . (1996) Human color vision.
    14. 14)
      • Itti, L., Baldi, P.: `A principled approach to detecting surprising events in video', Proc. IEEE Conf. on Computer Vision and Pattern Recognition (CVPR), June 2005, p. 631–637.
    15. 15)
    16. 16)
      • E. Niebur , C. Koch , R. Parasuraman . (1998) Computational architectures for attention, The attentive brain.
    17. 17)
      • P. Maragos , V.K. Madisetti , D.B. Williams . (1998) Noise suppression,, The digital signal processing handbook.
    18. 18)
      • F. Porikli , Y. Wang . Automatic video object segmentation using volume growing and hierarchical clustering. EURASIP J. Appl. Signal Process. , 6 , 814 - 832
    19. 19)
      • T. Watanabe , Y. Sasaki , S. Miyauchi , B. Putz , N. Fujimaki , M. Nielsen , R. Takino , S. Miyakawa . Attention-regulated activity in human primary visual cortex. J. Neurophysiol. , 2218 - 2221
    20. 20)
      • Rapantzikos, K., Tsapatsoulis, N., Avrithis, Y.: `Spatiotemporal visual attention architecture for video analysis', Proc. IEEE Int. Workshop On Multimedia Signal Processing (MMSP'04), 2004, p. 83–86.
    21. 21)
      • M. Pardàs , E. Sayrol . Motion estimation based tracking of active contours. Pattern Recognit. Lett. , 1447 - 1456
    22. 22)
      • C.-W. Ngo , T.-C. Pong , H.-J. Zhang . Motion analysis and segmentation through spatio-temporal slices processing. IEEE Trans. Image Process. , 3 , 341 - 355
    23. 23)
    24. 24)
    25. 25)
      • Liu, F., Picard, R.W.: `Finding periodicity in space and time', Proc. IEEE Int. Conf. Comput. Vis., 1998, p. 376–383.
    26. 26)
      • P. Joly , H.K. Kim . Efficient automatic analysis of camera work and microsegmentation of video using spatiotemporal images. Signal Process. Image Commun. , 295 - 307
    27. 27)
    28. 28)
      • K. Rapantzikos , N. Tsapatsoulis . (2003) On the implementation of visual attention architectures, Tales of the disappearing computer.
    29. 29)
    30. 30)
    31. 31)
      • N. Otsu . A thresholding selection method from gray-scale histogram. IEEE Trans. Syst. Man Cybernet. , 62 - 66
    32. 32)
      • B. Jähne . (1991) Spatio-temporal image processing: theory and scientific applications.
    33. 33)
      • http://homepages.inf.ed.ac.uk/rbf/CAVIAR/EC Funded CAVIAR project/IST 2001 37540.
    34. 34)
      • A. Torralba . Contextual priming for object detection. Int. J. Comput. Vis. , 2 , 169 - 191
    35. 35)
      • http://www.cs.brown.edu/people/black/regression.html.
    36. 36)
    37. 37)
      • Rutishauer, U., Walther, D., Koch, C., Perona, P.: `Is bottom-up attention useful for object recognition?', CVPR'04, July 2004, p. 37–44.
    38. 38)
      • P.A. Brandley , M.W. Stentiford . Visual attention for region of interest coding in JPEG 2000. J. Vis. Commun. Image Represent. , 232 - 250
    39. 39)
    40. 40)
    41. 41)
    42. 42)
      • M.J. Black , P. Anan . The robust estimation of multiple motions: parametric and piecewise-smooth flow fields. Comput. Vis. Image Underst. , 1 , 75 - 104
    43. 43)
      • H. Pashler . Attention and performance. Annu. Rev. Psychol. , 629 - 651
    44. 44)
      • E. Dickmanns , Blake , Yuille . (1992) Expectation-based dynamic scene understanding, Active vision.
    45. 45)
      • S. Sarkar , D. Majchrzak , K. Korimilli . Perceptual organization based computational model for robust segmentation of moving objects. Comput. Vis. Image Underst. , 3 , 141 - 170
    46. 46)
    47. 47)
      • E.H. Adelson , J. Bergen . Spatiotemporal energy models for the perception of motion. J. Opt. Soc. Am. , 2 , 284 - 299
    48. 48)
      • B.K.P. Horn , B.G. Schunck . Determining optical flow. Artif. Intell. , 185 - 203
    49. 49)
      • A.M. Treisman , G. Gelade . A feature integration theory of attention. Cognit. Psychol. , 1 , 97 - 136
    50. 50)
      • J. Serra . (1992) Image analysis and mathematical morphology.
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