计算机工程 ›› 2020, Vol. 46 ›› Issue (6): 241-247.doi: 10.19678/j.issn.1000-3428.0054809

• 图形图像处理 • 上一篇    下一篇

基于边界和中心关系的显著性检测方法

郭伟, 洪倩   

  1. 辽宁工程技术大学 软件学院, 辽宁 葫芦岛 125105
  • 收稿日期:2019-05-05 修回日期:2019-06-12 发布日期:2019-06-21
  • 作者简介:郭伟(1970-),女,副教授、硕士,主研方向为显著性检测;洪倩,硕士研究生。
  • 基金项目:
    国家自然科学基金(61172144);辽宁省教育厅科学技术研究项目(LJ2017QL032)。

Saliency Detection Method Based on Relation Between Boundary and Center

GUO Wei, HONG Qian   

  1. Software College, Liaoning Technical University, Huludao, Liaoning 125105, China
  • Received:2019-05-05 Revised:2019-06-12 Published:2019-06-21

摘要: 为提高显著性检测模型生成显著图时的准确率和对比度,提出一种基于边界和中心关系的显著性检测方法。对图像进行引导滤波平滑处理并利用SLIC实现超像素分割,根据中心点和边界点的关系计算超像素块的显著度,通过伽马变换背景抑制得到显著图1。利用边界点和中心点的关系得到种子点,改进流行排序算法,通过伽马变换背景抑制得到显著图2。将2幅显著图在像素级上进行融合,以得到最终显著图。实验结果表明,相对COV、DSR和GR等方法,该方法的F-Measure、E-Measure及MAE指标值更优,且能够提升背景抑制效果。

关键词: 显著性检测, 边界中心关系, 超像素, 流行排序, 引导滤波

Abstract: To improve the accuracy and contrast of the saliency graph generated by saliency detection models,this paper proposes a saliency detection method based on relation between boundary and center.First,the image is smoothed by guided filters and segmented by SLIC superpixel.Second,the saliency of the superpixel block is calculated according to the relation between the center point and the boundary point,and salient figure 1 is obtained by using background suppression through gamma transformation.At the same time,according to the relationship between the boundary point and the center point,the seed points are obtained,and the manifold ranking algorithm is improved.On this basis the salient figure 2 is obtained by using background suppression through gamma transformation.Finally,the two salient images are fused at the pixel level to obtain the final salient image.Experimental results show that the proposed method outperforms existing methods like COV,DSR and GR in terms of F-Measure,E-Measure,MAE and other indicators,improving background suppression effects.

Key words: saliency detection, relation between boundary and center, superpixel, manifold ranking, guided filtering

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