基于信息弥散机制的图像显著性区域提取算法

doi:10.6040/j.issn.1672-3961.0.2015.176

山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (6): 1-6.doi: 10.6040/j.issn.1672-3961.0.2015.176

• 机器学习与数据挖掘 • 下一篇

基于信息弥散机制的图像显著性区域提取算法

任永峰^1,2, 周静波¹

1. 淮阴工学院计算机与软件工程学院, 江苏淮安 223003;
2. 河海大学计算机与信息学院, 江苏南京 211100

收稿日期:2015-06-08 修回日期:2015-11-02 出版日期:2015-12-20 发布日期:2015-06-08
作者简介:任永峰(1980-),男,山东菏泽人,讲师,博士研究生,主要研究方向为图像分析.E-mail:renyongfeng@hyit.edu.cn
基金资助:
江苏省高校自然科学研究面上资助项目(14KJB520006)

An image saliency object detection algorithm based on information diffusion

REN Yongfeng^1,2, ZHOU Jingbo¹

1. Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huai'an 223003, Jiangsu, China;
2. College of Computer and Information, Hohai University, Nanjing 211100, Jiangsu, China

Received:2015-06-08 Revised:2015-11-02 Online:2015-12-20 Published:2015-06-08

摘要/Abstract

摘要： 为了更好提取图像的显著性区域,提出基于信息弥散机制的图像显著性区域检测算法。在所提算法中,首先将图像分割成超像素,根据图像中显著性区域频率变化比较大的特性,生成图像显著性区域的高频节点;然后针对高频节点利用凸包运算寻找显著性区域的种子节点,最后使用二阶高斯-马尔科夫随机场信息弥散方法在图像中对种子节点进行显著性区域信息扩散,得到图像的显著性区域。试验结果表明,利用二次规划求解每个数据之间的线性关系进行信息扩散,能够达到避免阈值选择和信息精准分类的效果,其结果优于同类的图像显著性区域检测算法。

关键词: 信息弥散, 高斯-马尔科夫随机场, 高频节点, 显著性检测, 凸包运算

Abstract: In order to better extract salient regions in images, we proposed an image salient region detection algorithm based on information diffusion mechanism. The proposed algorithm was divided into three steps. First, we segmented an input image into superpixels which were represented as the nodes in a graph. The node with high frequency was generated by the characteristics of the salient regions. Then, according to high-frequency nodes, convex hull computation was used to generate the saliency seeds of the salient object area. Finally, based on the seeds obtained by convex hull computation, the second-order Gaussian-Markov random fields were used to diffuse the information from saliency seeds to others, thereby forming the saliency region for a given image. The experimental results showed that the quadratic programming solution exploited to compute the weights between the nodes can effectively avoid threshold selection and enhance robustness accordingly. In addition, the proposed method performed better than the other state-of-the-art methods.

Key words: convex hull computation, information diffusion, saliency detection, Gaussian-Markov random fields, high frequency node

中图分类号:

TP301.6

任永峰, 周静波. 基于信息弥散机制的图像显著性区域提取算法[J]. 山东大学学报(工学版), 2015, 45(6): 1-6.

REN Yongfeng, ZHOU Jingbo. An image saliency object detection algorithm based on information diffusion[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2015, 45(6): 1-6.

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基于信息弥散机制的图像显著性区域提取算法

An image saliency object detection algorithm based on information diffusion

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