结合局部特征及全局特征的显著性检测

蔡强; 郝佳云; 曹健; 李海生

doi:10.3788/OPE.20172503.0772

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结合局部特征及全局特征的显著性检测

信息科学 | 更新时间：2020-07-07

- 结合局部特征及全局特征的显著性检测
- Salient detection via local and global feature
- 光学精密工程 2017年25卷第3期页码：772-778
- 作者机构：
  
  北京工商大学计算机与信息工程学院, 北京 100048
- 作者简介：
  
  [ "蔡强 (1969-), 男, 重庆永川人, 博士, 教授, 1991年、2003年于北京航空航天大学分别获得学士、博士学位, 1994年于北京轻工业学院获得硕士学位, 现为北京工商大学计算机与信息工程学院院长, 主要从事计算机图形学、计算几何、科学可视化、智能信息处理等方面的研究。E-mail:caiq@th.btbu.edu.cn" ]
  郝佳云 (1993-), 女, 山西长治人, 学士, 2015年于北京工商大学获得学士学位, 主要从事显著性检测、知识图谱等方面的研究。E-mail:haojiayun_happy@163.com HAO Jia-yun, E-mail:haojiayun_happy@163.com
- 基金信息：
  
  北京市教委科研计划一般项目(SQKM201610011010);北京市自然科学基金资助项目(4162019);北京市科技计划课题(Z161100001616004)
- DOI：10.3788/OPE.20172503.0772
  中图分类号： TP391
- 收稿日期：2016-12-21，
  
  录用日期：2017-1-15，
  
  纸质出版日期：2017-03-25
- 稿件说明：
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蔡强, 郝佳云, 曹健, 等. 结合局部特征及全局特征的显著性检测[J]. 光学精密工程, 2017,25(3):772-778.

Qiang CAI, Jia-yun HAO, Jian CAO, et al. Salient detection via local and global feature[J]. Optics and precision engineering, 2017, 25(3): 772-778.
蔡强, 郝佳云, 曹健, 等. 结合局部特征及全局特征的显著性检测[J]. 光学精密工程, 2017,25(3):772-778. DOI： 10.3788/OPE.20172503.0772.

Qiang CAI, Jia-yun HAO, Jian CAO, et al. Salient detection via local and global feature[J]. Optics and precision engineering, 2017, 25(3): 772-778. DOI： 10.3788/OPE.20172503.0772.

摘要

针对目前大多数显著性检测方法中采用背景种子以及局部区域对比度显著性检测模型的缺点，本文提出了一种综合考虑局部特征以及全局特征的显著性检测算法。在对图像进行分割之后，算法首先融合了采用多特征方式生成的背景显著图与采用前景区域对比度方式生成的前景显著图，之后使用高斯滤波器对融合后的结果进行优化形成局部特征显著图。其次，在局部特征显著图的基础上提取多种特征的样本集合进行训练，从而得到全局特征显著图。算法最后将第一步生成的局部特征显著图与全局特征显著图进行结合生成最终的显著图。实验部分验证了算法各部分的有效性，并且在3个公开数据集上对文章方法与近年来优秀的显著性检测算法进行了对比，实验结果显示，本文算法在CSSD数据集上的准确率、召回率以及F-measure分别达到了0.837 5、0.743 4和0.813 7，在其它数据集上也有良好表现。实验表明，本文算法能够有效抑制背景区域，并且高亮前景区域，更好地检测出显著目标。

Abstract

Due to the most of existing salient detection methods have some disadvantages on using background seeds and local area contrast salient detection model

a visual saliency detection algorithm named salient detection

which combines local feature and global feature

was proposed. After image segmentation

the algorithm firstly applied a background image created by multi-feature methods and a foreground saliency image created by foreground area contrast method

then

the fusion results was optimized by using Gaussian filter and the local feature saliency image was formed. Secondly

the sample set of various features was collected based on the local feature saliency image for practice and finally the global feature saliency image was obtained. At last

it combined the local feature saliency image produced in the first step with the global feature saliency image and created the final saliency image. In part of experiment

the proposed algorithm showed great results of precision

recall rate and F-measure on CSSD data set

with values of 0.837 5

0.743 4 and 0.813 7 respectively

the performance on other data set was also perfect. The results show that the proposed algorithm can effectively suppress the background area

highlight foreground area and detect the salient target better.

关键词

Keywords

references

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