基于背景感知的显著性目标检测算法
作者:
作者单位:

作者简介:

通讯作者:

中图分类号:

基金项目:

国家自然科学基金(61379036,61502430);国家自然科学基金委中丹合作项目(61361136002);浙江省重大科技专项重点工业项目(2014C01047);浙江理工大学521人才培养计划(20150428)


Saliency Detection Algorithm Based on Background Awareness
Author:
Affiliation:

Fund Project:

  • 摘要
  • |
  • 图/表
  • |
  • 访问统计
  • |
  • 参考文献
  • |
  • 相似文献
  • |
  • 引证文献
  • |
  • 增强出版
  • |
  • 文章评论
    摘要:

    在显著性目标检测算法中,流形排序的检测方法存在先验背景假设和目标检测不完整的问题.针对该问题,在流形排序算法基础上,融入背景鉴别、BING特征估计和权重调整,提出了一种基于背景感知的显著性目标检测算法.首先,通过计算颜色聚类后的边界区域的综合差异度,得到真实背景种子点,从而感知到真实背景区域;再结合图像的BING特征与初始显著图信息,获取目标位置,从而得到完整的前景种子点区域;然后重构前景区域的图模型且利用加权k-壳分解法,来调整前景区域节点之间的连接权重,进而获得清晰的目标边界.实验结果表明,同当前经典的一些算法比较,本文算法在准确率、召回率、F-measure和平均MAE上都优于其余算法.

    Abstract:

    In the saliency detection algorithm, there are some problems in the detection of the manifold ranking, such as the over ideal of the background and the incomplete target detection. Aiming at these problems, this study incorporated background identification, BING feature estimation, and weight adjustment in traditional manifold rank algorithm, and a method was proposed based on background awareness. Firstly, through the adaptive color clustering of the boundary area and calculating the synthetic difference degree to get the real background seed point, the real background areas were sensed. Then the BING feature of the image was calculated and the saliency map information was combined to obtain the target position, so as to obtain the complete foreground seed point area. Next, by reconstructing the graph model of the foreground region and using the weighted k-shell decomposition method, we adjusted the connection weight between the nodes in the foreground region to obtain a clear target boundary. The experimental results show that the proposed algorithm is superior to other algorithms in terms of precision, recall, F-measure, and average MAE compared with some classical algorithms.

    参考文献
    相似文献
    引证文献
引用本文

包晓安,朱晓芳,张娜,高春波,胡玲玲,桂江生.基于背景感知的显著性目标检测算法.计算机系统应用,2018,27(6):103-110

复制
分享
文章指标
  • 点击次数:
  • 下载次数:
  • HTML阅读次数:
  • 引用次数:
历史
  • 收稿日期:2017-09-26
  • 最后修改日期:2017-10-24
  • 录用日期:
  • 在线发布日期: 2018-05-29
  • 出版日期:
您是第位访问者
版权所有:中国科学院软件研究所 京ICP备05046678号-3
地址:北京海淀区中关村南四街4号 中科院软件园区 7号楼305房间,邮政编码:100190
电话:010-62661041 传真: Email:csa (a) iscas.ac.cn
技术支持:北京勤云科技发展有限公司

京公网安备 11040202500063号