东北大学学报:自然科学版 ›› 2018, Vol. 39 ›› Issue (10): 1380-1384.DOI: 10.12068/j.issn.1005-3026.2018.10.003

• 信息与控制 • 上一篇    下一篇

多目标显著性区域提取算法

孟琭, 陈妹雅   

  1. (东北大学 信息科学与工程学院, 辽宁 沈阳110819)
  • 收稿日期:2017-06-27 修回日期:2017-06-27 出版日期:2018-10-15 发布日期:2018-09-28
  • 通讯作者: 孟琭
  • 作者简介:孟琭(1982-),男,辽宁沈阳人,东北大学副教授,博士.冯明杰(1971-), 男, 河南禹州人, 东北大学副教授; 王恩刚(1962-), 男, 辽宁沈阳人, 东北大学教授,博士生导师.
  • 基金资助:
    国家自然科学基金资助项目(61101057).国家自然科学基金资助项目(51171041).

Salient Region Extraction Algorithm for Multi-target

MENG Lu, CHEN Mei-ya   

  1. School of Information Science & Engineering, Northeastern University, Shenyang 110819, China.
  • Received:2017-06-27 Revised:2017-06-27 Online:2018-10-15 Published:2018-09-28
  • Contact: MENG Lu
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摘要: 结合对象估计和超像素分割,提出面向多目标的显著性区域提取算法.首先,应用对象估计对图像中的多目标作初步检测,得到若干个显著性区域的初步结果;然后,再将这些显著性区域与超像素分割的结果作图像拼接,完善这些显著性区域;最后,将图像拼接的结果二值化,作为多目标显著性区域提取的最终结果.结果表明:所提算法可实现面向多目标的显著性区域提取.与3个经典算法的比较结果表明:所提算法在面向多目标显著性区域提取时更优.

关键词: 多目标, 显著性区域, 对象估计, 超像素分割, 图像处理

Abstract: Combining object estimation and super-pixel segmentation, a salient region extraction algorithm for multi-target was proposed. First, object estimation was used to make a preliminary extraction of multi-target in image, and the preliminary results of several salient regions were obtained. Then, these several salient regions were concatenated with the results of super-pixel segmentation to complete the saliency extraction. Finally, the concatenated regions were binarized as the final results of salient region for multi-target. The results showed that the proposed algorithm can achieve multi-target salient region extraction. The comparison with three classical algorithms indicated that the proposed algorithm is better when it is faced with salient region extraction for multi-target.

Key words: multi-target, salient region, object estimation, super-pixel segmentation, image processing

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