A JPEG codec adaptive to region importance
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
A Model of Saliency-Based Visual Attention for Rapid Scene Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Analysis and Mathematical Morphology
Image Analysis and Mathematical Morphology
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Objective evaluation of video segmentation quality
IEEE Transactions on Image Processing
Perceptually-weighted evaluation criteria for segmentation masks in video sequences
IEEE Transactions on Image Processing
EURASIP Journal on Advances in Signal Processing
Towards cognitive image fusion
Information Fusion
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Video object segmentation is a task that humans perform efficiently and effectively, but which is difficult for a computer to perform. Since video segmentation plays an important role for many emerging applications, as those enabled by the MPEG-4 and MPEG-7 standards, the ability to assess the segmentation quality in view of the application targets is a relevant task for which a standard, or even a consensual, solution is not available. This paper considers the evaluation of overall segmentation partitions quality, highlighting one of its major components: the contextual relevance of the segmented objects. Video object relevance metrics are presented taking into account the behaviour of the human visual system and the visual attention mechanisms. In particular, contextual relevance evaluation takes into account the context where an object is found, exploiting, for instance, the contrast to neighbours or the position in the image. Most of the relevance metrics proposed in this paper can also be used in contexts other than segmentation quality evaluation, such as object-based rate control algorithms, description creation, or image and video quality evaluation.