Abstract
Even though the study of saliency for color images has been thoroughly investigated in the past, very little attention has been given to datasets that cannot be displayed on traditional computer screens such as spectral images. Nevertheless, more than a means to predict human gaze, the study of saliency primarily allows for measuring informative content. Thus, we propose a novel approach for the computation of saliency maps for spectral images. Based on the Itti model, it involves the extraction of both spatial and spectral features, suitable for high dimensionality images. As an application, we present a comparison framework to evaluate how dimensionality reduction techniques convey information from the initial image. Results on two datasets prove the efficiency and the relevance of the proposed approach.
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Treisman, A., Gelade, G.: A feature-integration theory of attention. Cognitive Psychology 12, 97–136 (1980)
Koch, C., Ullman, S.: Shifts in selective visual attention: towards the underlying neural circuitry. Hum. Neurobiol. 4, 219–227 (1985)
Itti, L., Koch, C., Niebur, E.: A model of saliency-based visual attention for rapid scene analysis. IEEE Trans. on Pattern Analysis and Machine Intelligence 20, 1254–1259 (1998)
Harel, J., Koch, C., Perona, P.: Graph-based visual saliency. Advances in Neural Information Processing Systems 19, 545 (2007)
Hou, X., Zhang, L.: Saliency detection: A spectral residual approach. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2007, pp. 1–8. IEEE, Los Alamitos (2007)
Bruce, N., Tsotsos, J.: Saliency, attention, and visual search: An information theoretic approach. Journal of Vision 9 (2009)
Cerf, M., Harel, J., Einhäuser, W., Koch, C.: Predicting human gaze using low-level saliency combined with face detection. Advances in Neural Information Processing Systems 20, 241–248 (2008)
Lee, C., Varshney, A., Jacobs, D.: Mesh saliency. In: ACM SIGGRAPH 2005 Papers, p. 666. ACM, New York (2005)
Grassmann, H.: On the theory of compound colors. Phil. Mag. 7, 254–264 (1854)
Jia, X., Richards, J.: Segmented principal components transformation for efficient hyperspectral remote-sensing image display and classification. IEEE Trans. on Geoscience and Remote Sensing 37, 538–542 (1999)
Tyo, J., Konsolakis, A., Diersen, D., Olsen, R.: Principal-components-based display strategy for spectral imagery. IEEE Trans. on Geoscience and Remote Sensing 41, 708–718 (2003)
Du, Q., Yang, H.: Similarity-based unsupervised band selection for hyperspectral image analysis. IEEE Geoscience and Remote Sensing Letters 5, 564–568 (2008)
Poldera, G., van der Heijdena, G.: Visualization of spectral images. In: Proc. SPIE., vol. 4553, p. 133 (2001)
Jacobson, N., Gupta, M.: Design goals and solutions for display of hyperspectral images. IEEE Trans. on Geoscience and Remote Sensing 43, 2684–2692 (2005)
Zhang, H., Peng, H., Fairchild, M., Montag, E.: Hyperspectral image visualization based on a human visual model. In: Proceedings of SPIE, vol. 6806, p. 68060N (2008)
Nascimento, S., Ferreira, F., Foster, D.: Statistics of spatial cone-excitation ratios in natural scenes. Journal of the Optical Society of America A 19, 1484–1490 (2002)
Cai, S., Du, Q., Moorhead, R.: Hyperspectral imagery visualization using double layers. IEEE Trans. on Geoscience and Remote Sensing 45, 3028–3036 (2007)
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Le Moan, S., Mansouri, A., Hardeberg, J., Voisin, Y. (2011). Saliency in Spectral Images. In: Heyden, A., Kahl, F. (eds) Image Analysis. SCIA 2011. Lecture Notes in Computer Science, vol 6688. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21227-7_11
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DOI: https://doi.org/10.1007/978-3-642-21227-7_11
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