A method for single-stimulus quality assessment of segmented video
EURASIP Journal on Applied Signal Processing
Video object relevance metrics for overall segmentation quality evaluation
EURASIP Journal on Applied Signal Processing
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Image Communication
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Computer Vision and Image Understanding
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ICCSA '09 Proceedings of the International Conference on Computational Science and Its Applications: Part II
A neural approach to extract foreground from human movement images
Computer Methods and Programs in Biomedicine
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International Journal of Bio-Inspired Computation
A robust fully automatic scheme for general image segmentation
Digital Signal Processing
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Image and Vision Computing
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Image and Vision Computing
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Video segmentation assumes a major role in the context of object-based coding and description applications. Evaluating the adequacy of a segmentation result for a given application is a requisite both to allow the appropriate selection of segmentation algorithms as well as to adjust their parameters for optimal performance. Subjective testing, the current practice for the evaluation of video segmentation quality, is an expensive and time-consuming process. Objective segmentation quality evaluation techniques can alternatively be used; however, it is recognized that, so far, much less research effort has been devoted to this subject than to the development of segmentation algorithms. This paper discusses the problem of video segmentation quality evaluation, proposing evaluation methodologies and objective segmentation quality metrics for individual objects as well as for complete segmentation partitions. Both standalone and relative evaluation metrics are developed to cover the cases for which a reference segmentation is missing or available for comparison.