Abstract
Detection of salient objects is very useful for object recognition, content-based image/video retrieval, scene analysis and image/video compression. In this paper, we propose a color saliency model for salient objects detection in natural scenes. In our color saliency model, different color features are extracted and analyzed. For different color features, two efficient saliency measurements are proposed to compute different saliency maps. And a feature combination strategy is presented to combine multiple saliency maps into one integrated saliency map. After that, a segmentation method is employed to locate salient objects’ regions in scenes. Finally, a psychological ranking measurement is proposed for salient objects competition. In this way, we can obtain both salient objects and their rankings in one natural scene to simulate location shift in human visual attention. The experimental results indicate that our model is effective, robust and fast for salient object detection in natural scenes, also simple to implement.
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References
James, W.: The Principles of Psychology. Harvard University Press, Oxford (1890)
Koch, C., Ulman, S.: Shifts in Selection in Visual Attention: Toward the Underlying Neural Circuitry. Human Neurobiology 4(4), 219–227 (1985)
Aziz, M.Z., Mertsching, B.: Fast and Robust Generation of Feature Maps for Region-Based Visual Attention. IEEE Transaction on Image Processing 17(5), 633–644 (2008)
Liu, F., Gleicher, M.: Region Enhanced Scale-Invariant Saliency Detection. In: Proceeding of 2006 IEEE International Conference on Multimedia & Expo., pp. 1477–1480 (2006)
Liu, H., Jiang, S., Huang, Q., Xu, C., Gao, W.: Region-Based Visual Attention Analysis with Its Application in Image Browsing on Small Displays. In: Proceeding of 2007 ACM International Conference on Multimedia, pp. 305–308 (2007)
Hou, X., Zhang, L.: Saliency Detection: A Spectral Residual Approach. In: Proceeding of 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Hu, Y., Rajan, D., Chia, L.-T.: Robust Subspace Analysis for Detecting Visual Attention Regions in Images. In: Proceeding of the 13th annual ACM International Conference on Multimedia, pp. 716–724 (2005)
Treisman, A.M., 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 Transaction on Pattern Analysis and Machine Intelligence 20(11), 1254–1259 (1998)
Itti, L., Koch, C.: Computational Modelling of Visual Attention. Nature Reviews Neuroscience 2(3), 194–203 (2001)
Treisman, A., Gormican, S.: Feature analysis in early vision: evidence from search asymmetries. Psychology Review 95, 15–48 (1988)
Itten, J.: The Elements of Color. John Wiley & Sons Inc., New York (1961)
Mahnke, F.: Color, Environment, and Human Response. Van Nostrand Reinhold, Detroit (1996)
Ouerhani, N., Bur, A., Hügli, H.: Linear vs. Nonlinear Feature Combination for Saliency Computation: A Comparison with Human Vision. In: Franke, K., Müller, K.-R., Nickolay, B., Schäfer, R. (eds.) DAGM 2006. LNCS, vol. 4174, pp. 314–323. Springer, Heidelberg (2006)
Kitterler, J., Illingworth, J.: Minimum Error Thresholding. Pattern Recognition 19(1), 41–47 (1986)
Zabrodsky, H., Peleg, S.: Attentive transmission. Visual Communication and Image Representation 1(2), 189–198 (1990)
Liu, T., Sun, J., Zheng, N.-N., Tang, X., Shum, H.-Y.: Learning to Detect A Salient Object. In: Proceeding of the 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8 (2007)
Zhou, Q., Ma, L., Celenk, M., Chelberg, D.: Content-Based Image Retrieval Based on ROI Detection and Relevance Feedback. Multimedia Tools and Applications 27, 251–281 (2005)
Walther, D., Itti, L., Riesenhuber, M., Poggio, T., Koch, C.: Attentional Selection for Object Recognition - a Gentle Way. In: Bülthoff, H.H., Lee, S.-W., Poggio, T.A., Wallraven, C. (eds.) BMCV 2002. LNCS, vol. 2525, pp. 472–479. Springer, Heidelberg (2002)
Luo, J., Singhal, A., Etz, S.P., Gray, R.T.: A computational approach to determination of main subject regions in photographic images. Image Vision Computing 22(3), 227–241 (2004)
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Tian, M., Wan, S., Yue, L. (2010). A Color Saliency Model for Salient Objects Detection in Natural Scenes. In: Boll, S., Tian, Q., Zhang, L., Zhang, Z., Chen, YP.P. (eds) Advances in Multimedia Modeling. MMM 2010. Lecture Notes in Computer Science, vol 5916. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11301-7_26
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DOI: https://doi.org/10.1007/978-3-642-11301-7_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-11300-0
Online ISBN: 978-3-642-11301-7
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