Journal of the Korean Society for Precision Engineering (한국정밀공학회지)

Korean Society for Precision Engineering (한국정밀공학회)
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Implementation of Image Adaptive Map

적응적인 Saliency Map 모델 구현

  • Published : 2008.02.01
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Abstract

This paper presents a new saliency map which is constructed by providing dynamic weights on individual features in an input image to search ROI(Region Of Interest) or FOA(Focus Of Attention). To construct a saliency map on there is no a priori information, three feature-maps are constructed first which emphasize orientation, color, and intensity of individual pixels, respectively. From feature-maps, conspicuity maps are generated by using the It's algorithm and their information quantities are measured in terms of entropy. Final saliency map is constructed by summing the conspicuity maps weighted with their individual entropies. The prominency of the proposed algorithm has been proved by showing that the ROIs detected by the proposed algorithm in ten different images are similar with those selected by one-hundred person's naked eyes.

Keywords

References

  1. Anderson, J. R., 'Cognitive psychology and its implications,' Ewha Womans University Press, pp. 87-115, 2000
  2. Itti, L., Koch, C. and Niebur, E., 'Model of saliency-based visual attention for rapid scene analysis,' IEEE Transactions on Pattern Analysis and Machine Intelligence(PAMI), pp. 1254-1259, 1998
  3. Choi, K. J. and Lee, Y. B., 'Detecting Salient Regions based on Bottom - up Human Visual Attention Characteristic,' The Korean Institute of Information Scientists and Engineers(KIISE) : Software and application, Vol. 31, No. 2, pp. 189-202, 2004
  4. Son, J. I., Lee, M. H. and Shin, J. K., 'Implementation of saliency map model using independent component analysis,' The Korean sensors society, Vol. 10, No. 5, pp. 286-291, 2001
  5. Koch, C. and Ullman, S., 'Shifts in Selective Visual Attention : Towards the nderlying Neural Circuitry,' Human Neurobiology, Vol. 4, No. 4, pp. 219-227, 1985
  6. Milanese, R., Wechsler, H., Gil, S., Bost, J. and Pun, T., 'Integration of Bottom-up and Top-down Cues for Visual Attention Using Non-Linear Relaxation,' Proc. IEEE Conf. Computer Vision and Pattern Recognition, pp. 781-785, 1994
  7. Park, S. J., Shin, J. K. and Lee, M. H., 'Biologically Inspired Saliency Map Model for Bottom-up Visual Attention,' Lecture Notes in Computer Science, Vol. 2525, pp. 418-426, 2002
  8. Ouerhani, N., Bur, A. and Hügli, H., 'Linear vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision,' Lecture Notes in Computer Science, Vol. 4174, pp. 314-323, 2006
  9. Ouerhani, N. and Hügli, H., 'MAPS: Multiscale Attention-Based PreSegmentation of Color Images,' Lecture Notes in Computer Science, Vol. 2695, pp. 537-549, 2003
  10. Hu, Y., Xie, X., Ma, W. Y., Chia, L. T. and Rajan, D., 'Salient Region Detection Using Weighted Feature Maps Based on the Human Visual Attention Model,' Lecture Notes in Computer Science, Vol. 3332, pp. 993-1000, 2004
  11. Park, M. C. and Cheoi, K. J., 'A Motion - driven Selective Visual Attention System,' The Korea Contents Association, Vol. 5, No. 6, pp. 87-96, 2005
  12. Ouerhani, N. and Hügli, H., 'A Model of Dynamic Visual Attention for Object Tracking in Natural Image Sequences,' Lecture Notes in Computer Science, Vol. 2686, pp. 702-709, 2003
  13. Lee, T. W., Wachtler, T. and Sejnowski, T. J., 'Color Opponency is an Efficient Representation of Spectral Properties in Natural Scenes,' Vision Research, Vol. 42, Issue 17, pp. 2095-2103, 2002 https://doi.org/10.1016/S0042-6989(02)00122-0
  14. Kim, T. H. and Jeong, D. S., 'An Image Retrieval Technique using Entropy and Color Features,' The Korean Institute of Information Scientists and Engineers(KIISE), Vol. 26, No. 3, pp. 282-290, 1999