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Saliency detection based on color descriptor and high-level prior

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Abstract

The existing saliency detection methods calculate the Euclidean distance in CIElab color space as similarity degrees between image pixels or patches, although CIElab color owns a better perceptually uniform color difference, closing to human color perception. However, it may fail if salient objects consist of diverse color regions and are surrounded by cluttered backgrounds. Aiming at tackling the problem, we propose a background-based saliency detection method by exploring the new color descriptor and high-level prior. Specifically, here a novelty color space is produced to remedy the shortages of CIElab color space. Based on the global and local descriptors, two boundary-based saliency detection algorithms are individually performed, achieving the corresponding coarse saliency results. After that, we embed the center prior and objectness prior together into the two saliency results, respectively. To this end, L2 norm is applied to select the best saliency result. The experimental results on three benchmark datasets demonstrate the proposed method achieves competitive performance against several state-of-the-art methods under the comparison evaluation.

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Acknowledgements

This work was supported by the Natural Science Basic Research Plan of Shaanxi Province of China (Grant No. 2015JM6296).

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  1. Northwestern Polytechnical University, Xi’an, 710129, Shaanxi, China

    Fan Wang & Guohua Peng

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  1. Fan Wang
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  2. Guohua Peng
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Correspondence to Fan Wang.

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Wang, F., Peng, G. Saliency detection based on color descriptor and high-level prior. Machine Vision and Applications 32, 125 (2021). https://doi.org/10.1007/s00138-021-01250-1

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  • DOI: https://doi.org/10.1007/s00138-021-01250-1

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