Saliency detection combining foreground and background features based on manifold ranking

doi:10.11772/j.issn.1001-9081.2016.09.2560

Journal of Computer Applications ›› 2016, Vol. 36 ›› Issue (9): 2560-2565.DOI: 10.11772/j.issn.1001-9081.2016.09.2560

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Saliency detection combining foreground and background features based on manifold ranking

ZHU Zhengyu^1,2, WANG Mei¹

1. College of Computer Science, Chongqing University, Chongqing 400030, China;
2. Key Laboratory of Software Theory and Technology of Chongqing City, Chongqing 400030, China

Received:2016-03-04 Revised:2016-04-14 Online:2016-09-08 Published:2016-09-10
Supported by:
This work is partially supported by the National Key Technology Research and Development Program of China (2011BAH25B04).

基于Manifold Ranking和结合前景背景特征的显著性检测

朱征宇^1,2, 汪梅¹

1. 重庆大学计算机学院, 重庆 400030;
2. 软件理论与技术重庆市重点实验室, 重庆 400030

通讯作者: 汪梅
作者简介:朱征宇(1959-),男,安徽马鞍山人,教授,博士,主要研究方向:Web智能检索、数据挖掘、图像处理;汪梅(1992-),女,安徽宣城人,硕士研究生,主要研究方向:图像处理。
基金资助:
国家科技支撑计划重点项目（2011BAH25B04）。

Abstract

Abstract: Focusing on the issue that the saliency detection algorithm via graph-based manifold ranking (MR algorithm) is over dependent on background features extracted from boundary nodes, an improved saliency detection algorithm combined with foreground and background features was proposed. Firstly, an image was divided into several super-pixels and a close-loop model was constructed. Secondly, the foreground and background seeds were obtained by using manifold ranking algorithm according to foreground and background features. Then these two kinds of seed nodes were combined through brightness and color characteristics, resulting in more accurate query nodes. Finally, a saliency map of the image was obtained by computing the saliency value via manifold ranking algorithm. Experimental results show that compared with MR algorithm, the precision rate, the recall rate and the F-measure of the proposed algorithm are significantly improved, and the obtained saliency maps are much more close to the true value.

Key words: saliency detection, Manifold Ranking (MR), query node, saliency map, salient region

摘要： 针对基于图和流形排序（Manifold Ranking）的显著性检测算法（MR算法）过度依赖边界节点的背景特征的问题，提出一种改进的结合前景背景特征的显著性检测算法。首先，对图像进行超像素分割，建立闭环图模型；然后利用流形排序算法根据图像前景特征和背景特征分别得出前景种子和背景种子；再通过亮度和颜色特征对两类种子进行结合，筛选出更为准确的查询节点；最后再利用流形排序算法进行显著值计算，得到最终的显著图。实验表明，改进方法与MR算法相比在精确率、召回率、F值等多个评价指标上均有明显提升，得到的显著图更接近真值。

关键词: 显著性检测, 流形排序, 查询节点, 显著图, 显著区域

CLC Number:

TP391.41

ZHU Zhengyu, WANG Mei. Saliency detection combining foreground and background features based on manifold ranking[J]. Journal of Computer Applications, 2016, 36(9): 2560-2565.

朱征宇, 汪梅. 基于Manifold Ranking和结合前景背景特征的显著性检测[J]. 计算机应用, 2016, 36(9): 2560-2565.

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Saliency detection combining foreground and background features based on manifold ranking

基于Manifold Ranking和结合前景背景特征的显著性检测

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