Abstract
In this paper, we propose a saliency detection model based on amplitude spectrum. The proposed model first divides the input image into small patches, and then uses the amplitude spectrum of the Quaternion Fourier Transform (QFT) to represent the color, intensity and orientation distributions for each patch. The saliency for each patch is determined by two factors: the difference between amplitude spectrums of the patch and its neighbor patches and the Euclidian distance of the associated patches. The novel saliency measure for image patches by using amplitude spectrum of QFT proves promising, as the experiment results show that this saliency detection model performs better than the relevant existing models.
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References
Oppenheim, A.V., Lim, J.S.: The importance of phase in signals. Proc. IEEE 69, 529–541 (1981)
Piotrowski, L.N., Campbell, F.W.: A demonstration of the visual importance and flexibility of spatial-frequency amplitude and phase. Perception 11, 337–346 (1982)
Hou, X., Zhang, L.: Saliency Detection: A spectral residual approach. In: Proceedings of IEEE CVPR (2007)
Guo, C., Ma, Q., Zhang, L.: Spatio-temporal saliency detection using phase spectrum of quaternion fourier transform. In: Proceedings of IEEE CVPR (2008)
Oliva, A., Torralba, A.: Modeling the shape of the scene: a holistic representation of the spatial envelop. International Journal of Computer Vision 42, 145–175 (2001)
Treisman, A., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12(1), 97–136 (1980)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Bruce, N.D., Tsotsos, J.K.: Saliency based on information maximization. Advances in Neural Information Processing Systems 18, 155–162 (2006)
Brecht, M.D., Saiki, J.: A neural network implementation of a saliency map model. Neural Networks 19, 1467–1474 (2006)
Lu, Z., Lin, W., Yang, X., Ong, E., Yao, S.: Modeling visual attention’s modulatory aftereffects on visual sensitivity and quality evaluation. IEEE Transactions on Image Processing 14(11), 1928–1942 (2005)
Ell, T., Sangwin, S.: Hypercomplex Fourier Transforms of Color Images. IEEE Transactions on Image Processing 16(1), 22–35 (2007)
Engel, S., Zhang, X., Wandell, B.: Colour Tuning in Human Visual Cortex Measured With Functional Magnetic Resonance Imaging. Nature 388(6), 68–71 (1997)
Le Meur, O., Le Callet, P., Barba, D., Thoreau, D.: A coherent computational approach to model bottom-up visual attention. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI) 28(5), 802–817 (2006)
Liu, T., Sun, J., Zheng, N., Tang, X., Shum, H.Y.: Learning to detect a salient object. In: Proc. CVPR (2007)
Itti, L., Koch, C.: A saliency-based search mechanism for overt and covert shifts of visual attention. Visual Research 40, 1489–1506 (2000)
Gao, D., Vasconcelos, N.: Bottom-up saliency is a discriminant process. In: Proceedings of IEEE CVPR (2007)
Tatler, B.W., Baddeley, R.J., Gilchrist, I.D.: Visual correlates of fixation selection: effects of scale and time. Visual Search 45, 643–659 (2005)
Gopalakrishnan, V., Hu, Y., Rajan, D.: Salient region detection by modeling distributions of color and orientation. IEEE Transactions on Multimedia 11(5), 892–905 (2009)
Gonzalez, R.C., Woods, R.E.: Digital Image Processing, 3rd edn. Prentice-Hall, Englewood Cliffs
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. In: Proc. NIPS (2006)
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Fang, Y., Lin, W., Lee, BS., Lau, C.T., Lin, CW. (2011). Bottom-Up Saliency Detection Model Based on Amplitude Spectrum. In: Lee, KT., Tsai, WH., Liao, HY.M., Chen, T., Hsieh, JW., Tseng, CC. (eds) Advances in Multimedia Modeling. MMM 2011. Lecture Notes in Computer Science, vol 6523. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17832-0_35
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DOI: https://doi.org/10.1007/978-3-642-17832-0_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17831-3
Online ISBN: 978-3-642-17832-0
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