摘要
为了提高图像显著性检测的准确性,从数学模型上探索显著性的多特征空间.利用多尺度特征提取算法获得低层视觉特征,对特征矩阵用低秩矩阵恢复理论提取显著图,并在自底向上模型基础上融合了高层视觉特征,由高层视觉特征构成一幅权重的显著图.提高了显著度和显著目标的检测性能.通过自适应阈值算法对视觉显著目标进行分割.实验结果表明,该模型比传统的模型提取的显著目标更完整、更准确.
In order to improve the accuracy of image saliency detection, we explore the consistency of multi-features space from the view of mathematical model. We use the muhi- scale feature extraction algorithm to obtain the low level visual features, introduce the theory of low rank matrix recovery into the saliency map extraction, and incorporate the low level visual features and the high-level visual features. The high-level visual features are fused to compose a prior map and are treated as a prior term in the objective function to improve the performance. The image saliency objects are segmented by using the adaptive threshold algorithm. Extensive experiments show that our model can comfortably achieve more performance to the existing methods.
出处
《南华大学学报（自然科学版）》
2015年第3期73-77,共5页
Journal of University of South China：Science and Technology
基金
湖南省自然科学基金项目(2015JJ3110
13JJ9008)
湖南省教育厅基金资助项目(10C1144)
湖南省衡阳市科技计划基金资助项目(2011KG66)
关键词
彩色图像分割
视觉显著性
视觉特征
color image segmentation
visual saliency
visual features
