Visual saliency region detection by combination of soft- and hard-segmentation-wise approaches

Kanghan Oh; Kwanjong You

doi:10.1117/1.JEI.27.5.051204

12 February 2018 Visual saliency region detection by combination of soft- and hard-segmentation-wise approaches

Kanghan Oh, Kwanjong You

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Journal of Electronic Imaging, Vol. 27, Issue 5, 051204 (February 2018). https://doi.org/10.1117/1.JEI.27.5.051204

Abstract

Recent studies in saliency detection have exploited contrast value as a main feature and background prior as a secondary feature. To apply the background prior, most approaches are based on soft- or hard-segmentation mechanisms, and a significant improvement is seen. However, because of contrast feature usage, the soft-segmentation (SS)-wise models have many technical challenges when a high interobject dissimilarity exists. Although hard-segmentation-wise saliency models intuitively use the background prior without usage of the contrast feature, this model suffers from local noises due to undesirable discontinuous artifacts. By analyzing the drawbacks of the existing models, a combination saliency model, reflecting both soft- and hard-segmentation techniques is shown. The proposed model consists of the following three phases: SS-wise saliency, hard-segmentation-wise saliency, and a final saliency combination. In particular, we proposed an iterative reweighting processing for which an influence of outlier segmentation maps is decreased to improve the hard-segmentation-wise saliency. As shown in the experimental results, the proposed model outperforms the state-of-the-art models on various benchmark datasets, which consist of single, multiple, and complex object images.

CC BY: © The Authors. Published by SPIE under a Creative Commons Attribution 4.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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Kanghan Oh and Kwanjong You "Visual saliency region detection by combination of soft- and hard-segmentation-wise approaches," Journal of Electronic Imaging 27(5), 051204 (12 February 2018). https://doi.org/10.1117/1.JEI.27.5.051204

Received: 20 September 2017; Accepted: 9 January 2018; Published: 12 February 2018

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KEYWORDS

Performance modeling

Visualization

Image segmentation

Visual process modeling

Data modeling

Image processing

MATLAB

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