全局颜色对比度检测和分割显著图的方法

刘臣辰; 张建秋

Chinese

您当前的位置：

首页 >

文章列表页 >

全局颜色对比度检测和分割显著图的方法

更新时间：2026-09-04

- 全局颜色对比度检测和分割显著图的方法
- Saliency Map Detection and Segmentation Based on Global Color Contrast
- 微电子学与计算机 2017年34卷第12期页码：83-87
- 作者机构：
  
  复旦大学信息科学与工程学院智慧网络与系统研究中心和电子工程系,上海,200433
- 作者简介：
- 基金信息：
  
  国家自然科学基金 61571131
- DOI：
  中图分类号：
- 纸质出版：2017
- 稿件说明：
移动端阅览
刘臣辰, 张建秋. 全局颜色对比度检测和分割显著图的方法[J]. 微电子学与计算机, 2017, 34(12): 83-87.

LIU Chen-chen, ZHANG Jian-qiu. Saliency Map Detection and Segmentation Based on Global Color Contrast[J]. 2017, 34(12): 83-87.
刘臣辰, 张建秋. 全局颜色对比度检测和分割显著图的方法[J]. 微电子学与计算机, 2017, 34(12): 83-87. DOI：

LIU Chen-chen, ZHANG Jian-qiu. Saliency Map Detection and Segmentation Based on Global Color Contrast[J]. 2017, 34(12): 83-87. DOI：

摘要

为了有效地检测和分割图像中多个显著性目标

提出了全局颜色对比度检测和分割显著图的方法.该方法首先通过提取全局颜色对比度的特征

来获取初步的显著图

然后再进一步利用颜色离散度和空间信息进一步提高它们的显著性.以生成的显著图作为初始值

给出了利用迭代动态更新的阈值

来分割多个显著性目标的方法.利用公开Achanta数据库的实验结果表明此方法在ROC曲线上的表现优于文献报道中的方法

并且适用于多目标的检测与分割问题.

Abstract

In this paper

a novel saliency map detection and segmentation method is proposed to detect and segment multiple salient objects. The key idea behind this method is to abstract global color contrast feature to obtain preliminary saliency map

which is later improved by the color dispersion and space information. Based on the saliency map

this method is able to segment multiple salient objects using iteratively and dynamically computed threshold. The experimental results on Achanta database verify the effectiveness of this method for saliency detection and segmentation.

关键词

Keywords

references

Achanta R, Hemami S, Estrada F, et al. Frequency-tuned salient region detection[C]//Computer vision and pattern recognition, 2009. cvpr 2009. ieee conference on. IEEE, 2009: 1597-1604.

Liu T, Yuan Z, Sun J, et al. Learning to detect a salient object[J]. IEEE Transactions on Pattern analysis and machine intelligence, 2011, 33(2): 353-367. DOI: 353

Imamoglu N, Lin W, Fang Y. A saliency detection model using low-level features based on wavelet transform[J]. IEEE transactions on multimedia, 2013, 15(1): 96-105. DOI: 96

Itti L, Koch C, Niebur E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on pattern analysis and machine intelligence, 1998, 20(11): 1254-1259. DOI: 1254

Ma Q, Zhang L. Saliency-based image quality assessment criterion[C]//International Conference on Intelligent Computing. Springer Berlin Heidelberg, 2008: 1124-1133.

Bundy, Alan, Lincoln Wallen. Breadth-First Search[M]. Springer Berlin Heidelberg, 1984: 13.

Zhai Y, Shah M. Visual attention detection in video sequences using spatiotemporal cues[C]//Proceedings of the 14th ACM international conference on Multimedia. ACM, 2006: 815-824.

Hunter R S. Accuracy, precision, and stability of new photoelectric color-difference meter[J]. Journal of the Optical Society of America, 1948, 38(12): 1094. DOI: 1094

Bai P, Cui Z, Gan Z, et al. A novel saliency detection model based on curvelet transform[C]//Wireless Communications % Signal Processing (WCSP), 2016 8th International Conference on. IEEE, 2016: 1-5.

Hunter R S. Photoelectric color difference meter[J]. Josa, 1958, 48(12): 985-995. DOI: 985

Borji A, Cheng M M, Jiang H, et al. Salient object detection: A benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5706-5722. DOI: 5706

Cheng M M, Mitra N J, Huang X, et al. Global contrast based salient region detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(3): 569-582. DOI: 569

Luo Y, Yuan J, Xue P, et al. Saliency density maximization for efficient visual objects discovery[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2011, 21(12): 1822-1834. DOI: 1822

黄虹, 张建秋. 彩色自然场景统计显著图模型[J]. 复旦学报(自然科学版), 2014, 53(1): 51-58. DOI: 51

浏览量

下载量

CSCD

文章被引用时，请邮件提醒。

提交

工具集

关联资源

超低功耗实时时钟电路的设计

基于多尺度融合与小目标增强的航拍车辆检测算法

面向处理器分支预测单元的Spectre攻击轻量化防御方法

基于区块链的AI生成内容溯源框架设计研究

V型沟槽SiC中子探测器的蒙特卡洛分析