一种基于词袋模型的新的显著性目标检测方法
发布时间：2018-12-13 00:31
【摘要】：提出一种基于词袋模型的新的显著性目标检测方法.该方法首先利用目标性计算先验概率显著图,然后在图像的超像素区域内建立词袋模型,并基于此特征计算条件概率显著图,最后根据贝叶斯推断将先验概率和条件概率显著图进行合成.在ASD、SED以及SOD显著性目标公开数据库上与目前16种主流方法进行对比,实验结果表明本文方法具有更高的精度和更好的查全率,能够一致高亮地凸显图像中的显著性目标.
[Abstract]:A new significant target detection method based on word bag model is proposed. In this method, the priori probabilistic salience map is first calculated by objectiveness, then the word bag model is established in the super-pixel region of the image, and the conditional probabilistic significant map is calculated based on this feature. A priori probability and a conditional probabilistic salience diagram are combined according to Bayesian inference. The experimental results show that the proposed method has higher precision and better recall, and can consistently highlight the salient targets in the image by comparing with 16 popular methods in the open database of ASD,SED and SOD significant targets.
【作者单位】： 南通大学电气工程学院;南京理工大学计算机科学与工程学院;
【基金】：国家自然科学基金(61272220)资助~~
【分类号】：TP391.41
本文编号：2375525
[Abstract]:A new significant target detection method based on word bag model is proposed. In this method, the priori probabilistic salience map is first calculated by objectiveness, then the word bag model is established in the super-pixel region of the image, and the conditional probabilistic significant map is calculated based on this feature. A priori probability and a conditional probabilistic salience diagram are combined according to Bayesian inference. The experimental results show that the proposed method has higher precision and better recall, and can consistently highlight the salient targets in the image by comparing with 16 popular methods in the open database of ASD,SED and SOD significant targets.
【作者单位】： 南通大学电气工程学院;南京理工大学计算机科学与工程学院;
【基金】：国家自然科学基金(61272220)资助~~
【分类号】：TP391.41
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1 徐丹;唐振民;徐威;;融合颜色属性和空间信息的显著性物体检测[J];中国图象图形学报;2014年04期
，本文编号：2375525
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