摘要
图像处理技术依赖于高质量的视觉显著图才能获得较好的处理结果,现有的视觉显著性检测方法通常只能检测得到粗糙的视觉显著性属性图,严重影响了图像处理的最终效果。为此,提出一种采用贝叶斯理论和统计学习的视觉显著性检测方法来检测图像的视觉显著性属性。该方法基于贝叶斯理论的静态图像的自上而下的显著性和整体显著性,将自上而下的知识和由下向上的显著性进行结合;针对特征整合问题,利用线性模型的加权线性组合方法和正规化神经网络相结合的非线性加权方法来研究与所有因素相关的权值参数。根据自下而上的视觉显著性模型在两个标准数据集中采用ROC曲线来进行定量评价,结果表明非线性组合效果优于线性组合。
Image processing technology depends on the quality of the visual saliency map to obtain better results. The existing visual saliency detection method usually can only detect and get rough visual saliency attribute graph, seriously affecting the image processing results. This paper put forward a method of using Bayes theorem and statistical learning of visual saliency detection to detect the visual saliency property of image. The method was based on Bayes theorem of static image top- down significant and overall significance, and combined the top-down knowledge and the down-top significance. For the synthetic integration of characteristics, all the factors related to the weight parameter were studied by using linear model with the linear weighting combination method and regularized neural network combined with nonlinear weighting method. The ROC curves of the bottom-up visual saliency model in two fixed data set for quantitative evaluation, show that the effect of non-linear combination is better than that of linear combination.
出处
《计算机应用》
CSCD
北大核心
2012年第8期2288-2290,共3页
journal of Computer Applications
基金
湖南省教育厅科研基金资助项目(11C0009)
湖南省科技计划项目(2011GK3086
2011SK3079)
