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A contour self-compensated network for salient object detection

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Abstract

Given that existing salient object detection methods cannot effectively predict the fine contours of salient objects when extracting local or global contexts and features, we propose a novel contour self-compensated network (CSCNet) to generate a more accurate saliency map with complete contour. Unlike the common binary saliency detection, we reconstruct the salient object detection problem into a multi-classification problem of the background, the salient object, and the salient contour, where the salient contour is used as the third label for ground truth. Meanwhile, the image and its superpixel map are concatenated as the input of our network to add more edge information. Also, a penalty loss is proposed to restrict the spatial relationship between the background, objects, and their contours. Experimentally, we evaluate the proposed CSCNet on six benchmark datasets in both accuracy and efficiency and evaluate the attribute-based performance on the SOC dataset. Compared with 13 state-of-the-art algorithms, our CSCNet can detect salient objects more accurately and completely without adding too many convolutional layers and parameters.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China (No. 51905529), Shaanxi Province Natural Science-Based Research Program (2019JQ-295), and the UCAS PhD scholarship. Thanks to teacher Adams at NTU for providing equipment and to Yew Lee Tan for proofreading and modifying this paper.

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Authors and Affiliations

  1. Xi’an Institute of Optics and Precision Mechanics of CAS, Xi’an, China

    Yanan Wang, Huawei Wang & Jianzhong Cao

  2. University of Chinese Academy of Sciences, Beijing, China

    Yanan Wang

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  1. Yanan Wang
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  2. Huawei Wang
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Wang, Y., Wang, H. & Cao, J. A contour self-compensated network for salient object detection. Vis Comput 37, 1467–1479 (2021). https://doi.org/10.1007/s00371-020-01882-w

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