Objective and quantitative segmentation evaluation and comparison
Signal Processing
Robust Visual Method for Assessing the Relative Performance of Edge-Detection Algorithms
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image segmentation evaluation: A survey of unsupervised methods
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A neural approach to extract foreground from human movement images
Computer Methods and Programs in Biomedicine
Towards cognitive image fusion
Information Fusion
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The identification of objects in video sequences, that is, video segmentation, plays a major role in emerging interactive multimedia services, such as those enabled by the ISO MPEG-4 and MPEG-7 standards. In this context, assessing the adequacy of the identified objects to the application targets, that is, evaluating the segmentation quality, assumes a crucial importance. Video segmentation technology has received considerable attention in the literature, with algorithms being proposed to address various types of applications. However, the segmentation quality performance evaluation of those algorithms is often ad hoc, and a well-established solution is not available. In fact, the field of objective segmentation quality evaluation is still maturing; recently, some more efforts have been made, mainly following the emergence of the MPEG object-based coding and description standards. This paper discusses the problem of objective segmentation quality evaluation in its most difficult scenario: stand-alone evaluation, that is when a reference segmentation is not available for comparative evaluation. In particular, objective metrics are proposed for the evaluation of stand-alone segmentation quality for both individual objects and overall segmentation partitions.