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李宗民, 周晨晨, 宫延河, 刘玉杰, 李华. 结合域变换和轮廓检测的显著性目标检测[J]. 计算机辅助设计与图形学学报, 2018, 30(8): 1457-1465. DOI: 10.3724/SP.J.1089.2018.16778
引用本文: 李宗民, 周晨晨, 宫延河, 刘玉杰, 李华. 结合域变换和轮廓检测的显著性目标检测[J]. 计算机辅助设计与图形学学报, 2018, 30(8): 1457-1465. DOI: 10.3724/SP.J.1089.2018.16778
Li Zongmin, Zhou Chenchen, Gong Yanhe, Liu Yujie, Li Hua. Saliency Object Detection Based on Domain Transform and Contour Detection[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(8): 1457-1465. DOI: 10.3724/SP.J.1089.2018.16778
Citation: Li Zongmin, Zhou Chenchen, Gong Yanhe, Liu Yujie, Li Hua. Saliency Object Detection Based on Domain Transform and Contour Detection[J]. Journal of Computer-Aided Design & Computer Graphics, 2018, 30(8): 1457-1465. DOI: 10.3724/SP.J.1089.2018.16778

结合域变换和轮廓检测的显著性目标检测

Saliency Object Detection Based on Domain Transform and Contour Detection

  • 摘要: 针对多层显著性图融合过程中产生的显著目标边缘模糊、亮暗不均匀等问题,提出一种基于域变换和轮廓检测的显著性检测方法.首先选取判别式区域特征融合方法中的3层显著性图融合得到初始显著性图;然后利用卷积神经网络计算图像显著目标外部轮廓;最后使用域变换将第1步得到的初始显著性图和第2步得到的显著目标轮廓图融合.利用显著目标轮廓图来约束初始显著性图,对多层显著性图融合产生的显著目标边缘模糊区域进行滤除,并将初始显著性图中检测缺失的区域补充完整,得到最终的显著性检测结果.在3个公开数据集上进行实验的结果表明,该方法可以得到边缘清晰、亮暗均匀的显著性图,且准确率和召回率、F-measure,ROC以及AUC等指标均优于其他8种传统显著性检测方法.

     

    Abstract: In order to solve the problem of edge blur and brightness non-uniformity of salient object in theprocess of multi-level saliency maps integration, this paper proposes a saliency detection method based ondomain transform and contour detection. Firstly, we obtain initial saliency map by integrate three saliencymaps using DRFI method. Then, the salient object contour of image are computed by convolutional neuralnetwork. Finally, we use domain transform to blend the initial saliency map and the salient object contour.Under the constraints of the salient object contour, we can filter out the errors in the initial saliency map andcompensate the missed region. The experimental results on three public datasets demonstrates that ourmethod can produce a pixel-wised clearly saliency map with brightness uniformity and outperform othereight state-of-the-art saliency detection methods in terms of precision-recall curves, F-measure, ROC andAUC.

     

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