计算机科学 ›› 2015, Vol. 42 ›› Issue (8): 305-309.
周培云,李静,沈宁敏,庄毅
ZHOU Pei-yun, LI Jing, SHEN Ning-min and ZHUANG Yi
摘要: 随着图像采集技术的迅速发展,原始数字图像越来越清晰,已有的协同显著性检测方法在处理这些图像时所需的计算机内存也越来越大,并且伴随着很高的计算复杂性,严重影响了人机交互的实时性。因此,迫切需要一种快速的协同显著性检测方法。提出了一种基于图像分块与稀疏主特征提取的快速协同显著性检测方法(BSFCoS)。该方法在将图像均匀分割成若干个图像块的基础上,从Lab和RGB两种颜色空间上抽取底层特征,再使用截断幂(Truncated Power)的稀疏主成分分析方法进行稀疏主特征提取,以达到在最大程度保留原图像特征的同时减少特征点的数量与属性个数的效果。然后使用K-Means对提取的稀疏主特征进行聚类,并在聚类结果的基础上进行3种基于聚类的显著特征权值的计算。最后,将通过特征融合生成的单幅图像显著图和多幅图像显著图进行组合,以生成协同显著图。在Co-saliency Pairs与CMU Cornell iCoseg两个标准数据集上进行了实验仿真,实验结果表明,与其他协同显著性检测方法相比,BSFCoS在保证检测效果的同时大幅提高了针对多幅图像的协同显著性检测的速度。
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