Sequential interactive image segmentation
作者机构：TKLNDSTCollege of Computer ScienceNankai UniversityTianjinChina Computer Vision LabETH ZurichZurichSwitzerland
出 版 物：《Computational Visual Media》 (计算可视媒体(英文版))
年 卷 期：2023年第9卷第4期
页 面：753-765页
核心收录：
学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080203[工学-机械设计及理论] 0835[工学-软件工程] 0802[工学-机械工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
主 题：interactive segmentation user interaction object segmentation
摘 要：Interactive image segmentation(IIS)is an important technique for obtaining pixel-level *** many cases,target objects share similar ***,IIS methods neglect this connection and in particular the cues provided by representations of previously segmented objects,previous user interaction,and previous prediction masks,which can all provide suitable priors for the current *** this paper,we formulate a sequential interactive image segmentation(SIIS)task for minimizing user interaction when segmenting sequences of related images,and we provide a practical approach to this task using two pertinent *** first is a novel interaction *** annotating a new sample,our method can automatically propose an initial click proposal based on previous *** dramatically helps to reduce the interaction burden on the *** second is an online optimization strategy,with the goal of providing semantic information when annotating specific targets,optimizing the model with dense supervision from previously labeled *** demonstrate the effectiveness of regarding SIIS as a particular task,and our methods for addressing it.
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