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A Framework for Evaluating Video Object Segmentation Algorithms

机译:评估视频对象分段算法的框架

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Segmentation of moving objects in image sequences plays an important role in video processing and analysis. Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance. Many segmentation algorithms have been proposed along with a number of evaluation criteria. Nevertheless, no psychophysical experiments evaluating the quality of different video object segmentation results have been conducted. In this paper, a generic framework for segmentation quality evaluation is presented. A perceptually driven automatic method for segmentation evaluation is proposed and compared against an existing approach. Moreover, on the basis of subjective results, perceptual factors are introduced into the novel objective metric to meet the specificity of different segmentation applications such as video compression. Experimental results confirm the efficiency of the proposed evaluation criteria.
机译:图像序列中的移动对象的分割在视频处理和分析中起着重要作用。 Evaluating the quality of segmentation results is necessary to allow the appropriate selection of segmentation algorithms and to tune their parameters for optimal performance.已经提出了许多分割算法以及许多评估标准。然而,已经进行了评估不同视频对象分段结果的质量的有效实验。本文提出了一种用于分割质量评估的通用框架。提出了一种感知驱动的分割评估的自动方法,并与现有方法进行比较。此外,在主观结果的基础上,引入了感知因素,进入了新颖的客观度量,以满足不同分割应用的特异性,例如视频压缩。实验结果证实了所提出的评估标准的效率。

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