摘要
为了快速有效地完成多图像的协同显著性检测,提出了一种基于超像素匹配的检测模型。首先针对一般单个超像素特征匹配效果较差的问题,提出一种基于Hausdorff距离的邻域超像素集匹配算法来进行图像间超像素的精确匹配;然后构建图像内和图像间的双层元胞自动机模型,进行多幅图像之间的显著性传播,从而有效地检测出协同显著性。在公开的测试数据集上的实验结果表明,所提算法的检测精度和检测效率优于目前的主流算法,且具有较强的鲁棒性。
To effectively address the issue of multi-scene co-saliency detection,we proposed a novel model based on superpixel matching and cellular automata.First of all,we introduced an adjacent superpixel sets matching algorithm based on Hausdorff distance to achieve exact matching between image supperpixels.Comparing to the traditional superpixel matching algorithm,the new algorithm greatly improves the matching accuracy.In addition,we further proposed the2-layer cellular automata via intra image and inter images to carry out the significant propagation of multiple images,thus exploit the intrinsic relevance of similar regions through interactions with neighbors in multi-scene.Experimental results demonstrate that our model outperforms state-of-the-art methods.Furthermore,the proposed methods is efficient and robust.
出处
《计算机科学》
CSCD
北大核心
2017年第11期314-319,共6页
Computer Science
基金
国家自然科学基金(61501509)资助
