计算机科学 ›› 2014, Vol. 41 ›› Issue (10): 80-83.doi: 10.11896/j.issn.1002-137X.2014.10.018

• 2013’和谐人机环境联合学术会议 • 上一篇    下一篇

基于全局和局部短期稀疏表示的显著性检测

樊强,齐春   

  1. 西安交通大学电子与信息工程学院 西安710049;西安交通大学电子与信息工程学院 西安710049
  • 出版日期:2018-11-14 发布日期:2018-11-14
  • 基金资助:
    本文受国家自然科学基金(60972124),863项目(2009AA01Z321),973计划子课题(2010CB327902),高等学校博士学科点专项科研基金(20110201110012)资助

Saliency Detection Based on Global and Local Short-term Sparse Representation

FAN Qiang and QI Chun   

  • Online:2018-11-14 Published:2018-11-14

摘要: 显著性检测是计算机视觉研究的一个重要问题。提出了一种由底向上的基于稀疏表示的显著性检测新算法。一般显著性检测主要包含两个部分,即图像特征提取和显著性度量。对于一幅给定的图像,首先利用独立成分分析(ICA)方法提取图像特征,然后用一个局部和全局模型对图像进行显著性度量。其中,利用一种低秩表示方法提取全局显著性,以及利用一种稀疏编码方法提取局部显著性。最后融合局部和全局显著图得到最终的显著图。在一个人眼关注数据库上与目前几种流行的方法进行了对比实验,实验结果显示所提出的方法能够得到更高的视觉关注预测准确率。

关键词: 显著性检测,稀疏表示,低秩表示,稀疏编码

Abstract: Saliency detection has been considered to be an important issue in many computer vision tasks.We proposed a novel bottom-up saliency detection method based on sparse representation.Saliency detection includes two elements:image representation and saliency measurement.The two elements used in our method are both biological plausible and accurate.For an input image,we first used ICA algorithm to learn a set of basis functions,then the image could be represented by the set of basis functions.Next,we used a global and local saliency framework to measure the saliency respectively,and combined the two results to obtain the final saliency.The global saliency is obtained through Low-Rank Representation(LRR),and the local saliency is obtained through a sparse coding scheme.We compared our method with six state-of-the-art methods on two popular human eye fixation datasets.The experimental results indicate the accuracy of the proposed method to predict the human eye fixations is higher.

Key words: Saliency detection,Sparse representation,Low-rank representation,Sparse coding

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