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Bottom-up saliency estimation using sparse representation and structural redundancy reduction

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Abstract

As we all know that image saliency estimation is an important technique in many computer vision applications. We study bottom-up saliency estimation method based on sparse representation and structural redundancy reduction. Firstly, we learn a set of basis functions using independent component analysis on a large set of natural images and obtain image sparse coefficients. Secondly, we compute the Shannon entropy of the image sparse coefficients to represent its pixels information which build up the image information map. Finally, we discard the structural redundancy of the information map to yield the image saliency map. The performance of the proposed model is studied on natural scenes and psychophysical patterns, and evaluated with ground-truth data. The evaluation proves that the proposed model is highly consistent with the subjective visual attention.

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Acknowledgment

This research is supported by National Nature Science Foundation of China (Grant No: 60975007) and National High Technology Research and Development Program of China (“863” Program) (Grant No. 2013AA10230402). We would like to thank Ming-Ming Chen for providing the saliency maps of the state-of-the-art approaches for result comparison.

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Authors and Affiliations

  1. College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling Shaanxi, 712100, China

    Yan Long, DongJian He & Huaibo Song

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  1. Yan Long
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  2. DongJian He
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  3. Huaibo Song
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Correspondence to DongJian He.

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Long, Y., He, D. & Song, H. Bottom-up saliency estimation using sparse representation and structural redundancy reduction. Multimed Tools Appl 74, 9647–9663 (2015). https://doi.org/10.1007/s11042-014-2144-6

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  • DOI: https://doi.org/10.1007/s11042-014-2144-6

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