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Color boosted visual saliency detection and its application to image classification

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Abstract

For many applications in graphics, design and human computer interaction, it is essential to reliably estimate the visual saliency of images. In this paper, we propose a visual saliency detection method that combines the respective merits of color saliency boosting and global region based contrast schemes to achieve more accurate saliency maps. Our method is compared with existing saliency detection methods when evaluated using four public available datasets. Experimental results show that our method consistently outperformed current state-of-the-art methods on predicting human fixations. We also demonstrate how the extracted saliency map can be used for image classification.

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Acknowledgments

This work was supported by National Program on Key Basic Research Project (973 Program), under Grant No. 2012CB725305 and by The National Key Technology R&D Program of China, under Grant No. 2012BAH03F02.

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Authors and Affiliations

  1. Computer Science College, Zhejiang University, Hangzhou, 310027, China

    Bing Yang & Duanqing Xu

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  1. Bing Yang
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  2. Duanqing Xu
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Correspondence to Duanqing Xu.

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Yang, B., Xu, D. Color boosted visual saliency detection and its application to image classification. Multimed Tools Appl 69, 877–896 (2014). https://doi.org/10.1007/s11042-012-1148-3

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  • DOI: https://doi.org/10.1007/s11042-012-1148-3

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