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基于优化的ELM和深度层次的RGB-D显著检测

刘政怡 徐天泽

引用本文: 刘政怡, 徐天泽. 基于优化的ELM和深度层次的RGB-D显著检测[J]. 电子与信息学报, doi: 10.11999/JEIT180826 shu
Citation:  Zhengyi LIU, Tianze XU. RGB-D Saliency Detection Based on Optimized ELM and Depth Level[J]. Journal of Electronics and Information Technology, doi: 10.11999/JEIT180826 shu

基于优化的ELM和深度层次的RGB-D显著检测

    作者简介: 刘政怡: 女,1978年生,副教授,研究方向为计算机视觉;
    徐天泽: 男,1994年生,硕士生,研究方向为计算机视觉;
    通讯作者: 刘政怡, 22927463@qq.com
  • 基金项目: 安徽省自然科学基金(1908085MF182)

摘要: 目前,相当多的显著目标检测方法均聚焦于2D的图像上,而RGB-D图像所需要的显著检测方法与单纯的2D图像相去甚远,这就需要新的适用于RGB-D的显著检测方法。该文在经典的RGB显著检测方法,即极限学习机的应用的基础上,提出融合了特征提取、前景增强、深度层次检测等多种思路的新的RGB-D显著性检测方法。该文的方法是:第一,运用特征提取的方法,提取RGB图4个超像素尺度的4096维特征;第二,依据特征提取中产生的4个尺度的超像素数量,分别提取RGB图的RGB, LAB, LBP特征以及深度图的LBE特征;第三,根据LBE和暗通道特征两种特征求出粗显著图,并在4个尺度的遍历中不断强化前景、削弱背景;第四,根据粗显著图选取前景与背景种子,放入极限学习机中进行分类,得到第1阶段显著图;第五,运用深度层次检测、图割等方法对第1阶段显著图进行再次优化,得到第2阶段显著图,即最终显著图。

English

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  • 图 1  本算法程序流程图

    图 2  LBE部分显著图与本文对应显著图对比

    图 3  有无流程优化的P-R曲线对比

    图 4  部分图片不同方法显著图像对比

    图 5  各种方法的P-R曲线对比

    表 1  各算法F-measure值

    算法本文ELMACSDMGMRGPLBEDES
    F0.75260.58570.64640.72200.74080.2728
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文章相关
  • 通讯作者:  刘政怡, 22927463@qq.com
  • 收稿日期:  2018-08-22
  • 录用日期:  2019-05-15
  • 网络出版日期:  2019-06-03
通讯作者: 陈斌, bchen63@163.com
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