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2013 IEEE International Conference on Multimedia and Expo (ICME) (2013)
San Jose, CA, USA
July 15, 2013 to July 19, 2013
ISSN: 1945-7871
ISBN: 978-1-4799-0015-2
pp: 1-6
Xiaochun Cao , School of Computer Science and Technology, Tianjin University, 300072, China
Zhiqiang Tao , School of Computer Science and Technology, Tianjin University, 300072, China
Bao Zhang , School of Computer Science and Technology, Tianjin University, 300072, China
Huazhu Fu , School of Computer Science and Technology, Tianjin University, 300072, China
Xuewei Li , School of Computer Science and Technology, Tianjin University, 300072, China
ABSTRACT
Co-saliency is the common saliency existing in multiple images, which keeps consistent in saliency maps. One saliency detection method generates saliency maps for all the input images, so that we have a group of maps. Salient region of each image is extracted by its corresponding saliency map in the group. We use a matrix to combine all the salient regions. Ideally, these co-salient regions are similar and consistent, and therefore the matrix rank appears low. In this paper, we formalize this general consistency criterion as rankone constraint and propose a consistency energy to measure the approximation degree between matrix rank and one. We combine the single and multiple image saliency maps, and adaptively weight these maps under the rank-one constraint to generate the co-saliency map. Our method is valid for more than two input images and has more robustness than the existing co-saliency methods. Experimental results on benchmark database demonstrate that our method has the satisfactory performance on co-saliency detection.
INDEX TERMS
Image color analysis, Approximation methods, Visualization, Databases, Histograms, Equations, Robustness
CITATION

Xiaochun Cao, Zhiqiang Tao, Bao Zhang, Huazhu Fu and Xuewei Li, "Saliency map fusion based on rank-one constraint," 2013 IEEE International Conference on Multimedia and Expo (ICME), San Jose, CA, USA USA, 2013, pp. 1-6.
doi:10.1109/ICME.2013.6607523
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