多尺度下幅度谱与相位谱相融合的视觉注意建模  

Visual Attention Modeling Based on Multi-scale Fusion of Amplitude Spectrum and Phase Spectrum

作　　者：袁小艳[1,2] 王安志[1] 潘刚[2] 王明辉[1] 

机构地区：[1]四川大学计算机学院,成都610064 [2]四川文理学院智能制造学院,达州635000

出　　处：《计算机科学》2017年第7期293-298,共6页Computer Science

基　　金：国家重点研究与发展计划(2016YFB0700802;2016YFB0800600);国家海洋局海洋遥感工程技术研究中心创新青年项目(2015001);四川文理学院2015年度特色培育一般项目(2015TP001Y);四川文理学院智能制造产业技术开发研究专项项目(2017ZZ006Y)资助

摘　　要：针对现有大多数频域显著性检测算法仅单独使用频域幅度谱或相位谱的不足,提出了多尺度下频域幅度谱与相位谱相结合的视觉注意模型。该模型先对图像进行四元变换以得到幅度谱和相位谱,然后对幅度谱进行了伽马修正和高斯滤波,最后采用信息熵作为权重对多尺度显著图进行融合。在两个公开数据集Bruce和Judd上,采用ROC曲线、AUC值和F-Measure测量方法对算法进行了验证和评估。实验结果表明提出的算法优于现有的5种视觉注意模型,能够更准确地预测出人们注意的显著区域,取得了更令人满意的结果。Aiming at the deficiency of most existing frequency domain saliency detection algorithms which only use frequency domain amplitude spectrum or phase spectrum information alone,a visual attention model combining multi-scale frequency amplitude spectrum and phase spectrum was proposed.Firstly,the amplitude spectrum and the phase spectrum are obtained by quaternary transforming the image,and then the gamma correction and Gaussian filtering are performed on the amplitude spectrum.Finally,the entropy is used as weight to fuse the multi-scale saliency map.On the two published data sets,Bruce and Judd,the ROC curve,AUC value and F-Measure method were used to validate and evaluate the algorithm.The experimental results show that the proposed algorithm outperforms five visual attention models,predicts the significant attention areas more accurately,and achieves more satisfactory results.

关 键 词：多尺度 幅度谱 相位谱 信息熵 视觉注意 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]