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Saliency detection based on superpixel correlation and cosine window filtering

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Abstract

With the development of computer vision, image salient region detection plays an important role in the field of image processing. However, existing saliency detection methods can only generate saliency maps without achieving accuracy, thereby ignoring the integrity of the significant target. The present study proposed a saliency detection algorithm based on the correlation of superpixel and cosine window filtering (SPC) algorithm that introduced a variable weight superpixel segmentation method to keep the edge of information and enhance the integration of the salient target. Space distance and color distance are used to judge the correlation between two superpixels in the proposed algorithm, and based on the superpixel correlation, the saliency map is obtained by all superpixels’ voting. When the saliency map is corrected by a cosine window, we call this operation a cosine window filtering. Finally, we used the OSTU method for binarization and obtained the target position and size, using connected domain detection. The experiment results showed that the SPC algorithm can describe superpixels’ significance accurately and extract salient object. As compared with other algorithms, our algorithm offers higher accurate results, regarding the accurate position and size of salient targets.

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  1. School of Aerospace Science and Technology, Xidian University, Xi’an, 710071, China

    Yunyi Yan & Jiang Zhu

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  1. Yunyi Yan
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  2. Jiang Zhu
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Correspondence to Yunyi Yan.

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Yan, Y., Zhu, J. Saliency detection based on superpixel correlation and cosine window filtering. Multimed Tools Appl 78, 21205–21221 (2019). https://doi.org/10.1007/s11042-019-7407-9

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  • DOI: https://doi.org/10.1007/s11042-019-7407-9

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