基于颜色和运动空间分布的时空显著性区域检测算法  

Spatiotemporal saliency framework based on spatial distribution of color and motion

作　　者：郑云飞[1,2] 张雄伟[1] 曹铁勇[1] 杨吉斌[1] 

机构地区：[1]解放军理工大学指挥信息系统学院,南京210007 [2]陆军军官学院防空兵系,合肥230000

出　　处：《计算机应用研究》2017年第7期2189-2195,共7页Application Research of Computers

基　　金：国家自然科学基金资助项目(61471394);国家自然科学基金青年科学基金资助项目(61402519);江苏省自然科学基金资助项目(BK2012510;BK20140071;BK20140074)

摘　　要：针对复杂背景和运动条件下视频显著性区域检测准确度不高的问题,提出了一个新的时空一致性优化模型,并基于颜色空间分布和运动空间分布特征,结合时空一致性优化方法构建一个新的时空显著性区域检测模型,改进复杂运动和运动条件下视频显著性区域检测的效果。首先对视频帧进行超像素分割,然后提取三种具有互补性质的超像素级颜色空间分布特征和两种运动空间分布特征,再利用时空一致性分别融合优化空间显著特征和时间显著特征得到空间显著图和时间显著图。在时空融合阶段,利用时空一致性模型融合空间显著度和时间显著度得到超像素级的时空显著图。为进一步提高检测的准确度和完整度,构建一个能量最小化模型得到更精确的像素级时空显著图。通过与最新的视频显著性模型进行比较,算法有更好的检测效果,对复杂背景和运动条件有更强的鲁棒性。Aiming to the problem that previous spatiotemporal saliency region detection had unsatisfying performance under complex scenes and motion conditions, this paper proposed a novel spatiotemporal consistency optimization model.Meanwhile, it proposed a spatiotemporal saliency region detection model based on spatial distribution of color and motion using spatiotemporal consistency optimization.Firstly, it decomposed the video frame into a set of superpixels.Then, it extracted three complementary spatial distribution and two motion spatial distribution cues, then fused and optimized these saliency cues using spatiotemporal consistency optimization to derive spatial saliency map and temporal saliency map respectively.In spatiotemporal fusion phase, it derived the superpixel-level saliency map by fusing and optimizing the spatial and temporal saliency map using spatiotemporal consistency optimization.Lastly, it derived the pixel-level saliency map by solving a self-defined function energy minimization model.Compared with state of the art models, this model has better performance and stronger robust with complex scenes and motion conditions.

关 键 词：时空一致性优化 颜色的空间分布 运动的空间分布 时空显著性 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]