CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

An Improved Technique to Compute Visual Attention Map based upon Wavelet Domain

by Manish Mahajan, Navdeep Kaur
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 60 - Number 12
Year of Publication: 2012
Authors: Manish Mahajan, Navdeep Kaur
10.5120/9747-4313

Manish Mahajan, Navdeep Kaur . An Improved Technique to Compute Visual Attention Map based upon Wavelet Domain. International Journal of Computer Applications. 60, 12 ( December 2012), 36-42. DOI=10.5120/9747-4313

@article{ 10.5120/9747-4313,
author = { Manish Mahajan, Navdeep Kaur },
title = { An Improved Technique to Compute Visual Attention Map based upon Wavelet Domain },
journal = { International Journal of Computer Applications },
issue_date = { December 2012 },
volume = { 60 },
number = { 12 },
month = { December },
year = { 2012 },
issn = { 0975-8887 },
pages = { 36-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume60/number12/9747-4313/ },
doi = { 10.5120/9747-4313 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:06:25.338846+05:30
%A Manish Mahajan
%A Navdeep Kaur
%T An Improved Technique to Compute Visual Attention Map based upon Wavelet Domain
%J International Journal of Computer Applications
%@ 0975-8887
%V 60
%N 12
%P 36-42
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Visual system of human beings does not process the complete area of image rather focus upon limited area of visual image. But in which area does the visual attention focused is a topic of hot research nowadays. Research on psychological phenomenon indicates that attention is attracted to features that differ from its surroundings or the one that are unusual or unfamiliar to the human visual system. Detection of visually salient image regions is useful for applications like object segmentation, adaptive compression, and object recognition. Object or region based image processing can be performed more efficiently with information pertaining locations that are visually salient to human perception with the aid of a saliency map. The saliency map is a master topological map having the possible locations of objects or regions which a human perceived as important/salient. In this paper a method for computing the saliency map in wavelet transform domain has been proposed. Earlier works in this domain although calculated the saliency very efficiently but they just involve the approximation components of DWM, the detail components have not been taken care but in many applications these detailed components are of great importance to highlight the smaller but visually important components.

References
  1. B. Wandell, Foundations of Vision, Sunderland, MA: Sinauer, 1995.
  2. O. Hershler and S. Hochstein, "At first sight: a high-level pop out effects for faces," in Vision Research, vol. 45, no. 13, pp. 1707-1724, 2005.
  3. M. Cerf, J. Harel, W. Einhauser and C. Koch, "Predicting human gaze using low-level saliency combined with face detection," in Advances in neural information processing systems, vol. 20, pp. 241-248, 2008.
  4. K. C. Lai, S. C. Wong and K. Lun, "A rate control algorithm using human visual system for video conferencing systems," in Proc. Int. Conf. Signal Processing, vol. 1, pp. 656-659, Aug. 2002.
  5. L. Tong, K. R. Rao, "Region-of-interest based rate control for low-bit-rate video conferencing," in Journal of Electronic Imaging, vol. 15, no. 3, July, 2006.
  6. L. S. Karlsson, "Spatio-temporal pre-processing methods for region-of-interest video coding," PhD dissertation, Sundsvall, Sweden, 2007.
  7. C. -W. Tang, C. -H. Chen, Y. -H. Yu and C. -J. Tsai, "Visual sensitivity guided bit allocation for video coding," in IEEE Trans. Multimedia, vol. 8, pp. 11-18, Feb. 2006.
  8. K. Minoo and T. Q. Nguyen, "Perceptual video coding with H. 264," in Proc. 39th Asilomar Conf. Signals, Systems, and Computers, Nov. 2005.
  9. C. Huang, C. Lin, "A novel 4-D perceptual quantization modeling for H. 264 bit-rate control," in IEEE Trans. On Multimedia, vol. 9, no. 6, pp. 1113-1124, 2007.
  10. L. Itti, C. Koch, and E. Niebur, "A model of saliency-based visual attention for rapid scene analysis," in IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 20, pp. 1254-1259, Nov. 1998.
  11. L. Itti, and C. Koch, "Computational Modeling of visual attention," in Nature Reviews, Neuroscience, vol. 2, pp. 194-203,Mar, 2001.
  12. T. Liu, N. Zheng, W. Ding and Z. Yuan, "Video attention: Learning to detect a salient object sequence," in Proc. ICPR, pp. 1-4, 2008.
  13. L. Itti, "Automatic Foveation for Video Compression Using a Neurobiological Model of Visual Attention," in IEEE Trans on Image Processing, vol. 13, no. 10, pp. 1304-1318, 2004.
  14. L. Itti, "Automatic attention-based prioritization of unconstrained video for compression," In Proc. SPIE Human Vision and Electronic Imaging, vol. 5292, pp. 272-283, 2004.
  15. W. Lai, X. Gu, R. Wang, W. Ma, and H. Zhang, "A content-based bit allocation model for video streaming," in proc. IEEE international Conference on Multimedia and Expo (ICME), 2004.
  16. Z. Chen, G. Qiu, Y. Lu, L. Zhu, Q. Chen, X. Gu, and W. Charles, "Improving video coding at scene cuts using attention based adaptive bit allocation,", in Proc. ISCAS, pp. 3634-3638, 2007.
  17. L. Itti and C. Koch, "A Saliency-based Search Mechanism for Overt and Covert Shifts of Visual Attention," Vision Research 40, pp. 1489-1506,2000.
  18. D. Walther and C. Koch, "Modeling Attention to Salient Protoobjects,"Neural Networks 19, pp. 1395-1407,2006.
  19. N. Tsapatsoulis and K. Rapantzikos, "Wavelet Based Estimation of Saliency Maps in Visual Attention Algorithms", LNCS 4132, pp. 538-547, Springer-Verlag Berlin Heidelberg, 2006.
  20. Y. -F. Ma and H. -J. Zhang, "Contrast-based Image Attention Analysis by Using Fuzzy Growing", Proceedings of the 11th ACM International Conference on Multimedia, pp. 374-381, 2003.
  21. R. Achanta, F. Estrada, P. Wils, and S. Susstrunk, "Salient Region Detection and Segmentation", LNCS, Computer Vision Systems,Springer BerlinlHeidelberg, pp. 66-75, 2008.
  22. X. Hou and L. Zhang, "Saliency Detection: A Spectral Residual Approach", IEEE Conference on Computer Vision and Recognition 2007, pp. 1-8,2007.
  23. W. H. Ngau, L. -M. Ang, K. P. Seng, "Salient Region Detection Using Contrast-Based Saliency and Watershed Segmentation", Proceedings of the 2nd International Conference on Computing & Informatics 2009 (lCOCI09), pp. 475-479, 2009.
  24. U. Rajashekar, L. Cormack, and A. Boyik, "Visual Search: Structure from Noise", Proceedings of Eye Tracking Research Applications Symposium", New Orleans, Los Angeles, USA, pp. 119-123,2002.
  25. W. -J. Won, S. Jeong, and M. Lee, "Road Traffic Sign Saliency Map Model", Proceedings of Image and Vision Computing New Zealand 2007, New Zealand, pp. 91-96, 2007.
  26. A, Bamidele, F. Stentiford, and J. Morphett, "An Attention-based Approach to Content-Based Image Retrieval", BT Technology Journal, 22 (7), pp. 151-160, 2004.
  27. M. C. Park and K. J. Cheoi, "A Digital Image Watermarking Using A Feature-Driven Attention Module", Proceedings of Visualization,Image, and Image Processing, 2002.
  28. A. P. Bradley and W. M. Stentiford, "Visual Attention for Region of Interest Coding in JPEG 2000", Journal of Vision, Communication, and Image Representation, pp. 232-250,2003.
  29. R. Hong, W. Chao, W. Meng, and F. Sun, "Salience Preserving Multi-Focus Image Fusion With Dynamic Range Compression", International Journal of Innovative Computing, Information and Control ICIC International, Vol. 5, No. 8, 2009.
  30. S. Frintrop, M. Klodt, and E. Rome. A real-time visual attention system using integral images. International Conference on Computer Vision Systems, 2007.
  31. L. Itti and P. F. Baldi. Bayesian surprise attracts human attention. Advances in Neural Information Processing Systems,19:547–554, 2005.
  32. J. Harel, C. Koch, and P. Perona. Graph-based visual saliency. Advances in Neural Information Processing Systems, 19:545–552, 2007.
  33. T. Kadir, A. Zisserman, and M. Brady. An affine invariant salient region detector. European Conference on Computer Vision, 2004.
  34. C. Koch and S. Ullman. Shifts in selective visual attention: Towards the underlying neural circuitry. Human Neurobiology, 4(4):219–227, 1985.
  35. N. Bruce and J. Tsotsos. Attention based on information maximization. Journal of Vision, 7(9):950–950, 2007.
  36. B. Olshausen, C. Anderson, and D. Van Essen. A neurobiological model of visual attention and invariant pattern recognition based on dynamic routing of information. Journal of Neuroscience, 13:4700–4719, 1993.
  37. J. K. Tsotsos, S. M. Culhane,W. Y. K. Wai, Y. Lai, N. Davis, and F. Nuflo. Modeling visual attention via selective tuning. Artificial Intelligence, 78(1-2):507–545, 1995.
  38. R. Achanta, F. Estrada, P. Wils, and S. S¨usstrunk. Salient region detection and segmentation. International Conference on Computer Vision Systems, 2008.
  39. Y. Hu, X. Xie, W. -Y. Ma, L. -T. Chia, and D. Rajan. Salient region detection using weighted feature maps based on the human visual attention model. Pacific Rim Conference on Multimedia, 2004.
  40. L. Itti and C. Koch. Comparison of feature combination strategies for saliency-based visual attention systems. SPIE Human Vision and Electronic Imaging IV, 3644(1):473–482, 1999.
  41. R. Achanta, S. Hemami, F. Estrada, and S. Sllsstrunk, "Frequency tuned Salient Region Detection", IEEE International Conference on Computer Vision and Pattern Recognition (CVPR), 2009.
  42. C. Ngau, Li-Minn and K. Seng "Bottom up Visual Saliency Ma using Waveelet Transform Domain" 978-1-4244-5540-9/10/$26. 00 ©2010 IEEE.
  43. B. Zhang, Y. Zheng, and Q. Zhang, "Extracting Regions of Interest Based on Phase Spectrum and Morphological Approach",Proceedings of the International Colloquium on Computing, Communication, Control, and Management (ISECS), pp. 427-430, 2009.
  44. Bekerley Image Database: www. cs. berkeley. edul.
Index Terms

Computer Science
Information Sciences

Keywords

Saliency map wavelet transform approximation coefficients detail coefficients salient region

Powered by PhDFocusTM