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Image segmentation
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== Segmentation of related images and videos== {{main|Object co-segmentation}} Related images such as a photo album or a sequence of video frames often contain semantically similar objects and scenes, therefore it is often beneficial to exploit such correlations.<ref name="Vicente Rother Kolmogorov 2011 p. ">{{cite conference | last1=Vicente | first1=Sara | last2=Rother | first2=Carsten | last3=Kolmogorov | first3=Vladimir | title=CVPR 2011 | chapter=Object cosegmentation | publisher=IEEE | year=2011 | pages=2217β2224 | isbn=978-1-4577-0394-2 | doi=10.1109/cvpr.2011.5995530 }}</ref> The task of simultaneously segmenting scenes from related images or video frames is termed [[Object co-segmentation|co-segmentation]],<ref name="Liu Wang Hua Zhang 2018 pp. 5840β5853"/> which is typically used in [[Activity recognition|human action localization]]. Unlike conventional [[Minimum bounding box|bounding box]]-based [[object detection]], human action localization methods provide finer-grained results, typically per-image segmentation masks delineating the human object of interest and its action category (e.g., ''Segment-Tube''<ref name="Wang Duan Zhang Niu p=1657"/>). Techniques such as dynamic [[Markov random field|Markov Networks]], [[Convolutional neural network|CNN]] and [[Long short-term memory|LSTM]] are often employed to exploit the inter-frame correlations.
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