Feature Space Optimization For Semantic Video Segmentation
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)(2016)
摘要
We present an approach to long-range spatio-temporal regularization in semantic video segmentation. Temporal regularization in video is challenging because both the camera and the scene may be in motion. Thus Euclidean distance in the space-time volume is not a good proxy for correspondence. We optimize the mapping of pixels to a Euclidean feature space so as to minimize distances between corresponding points. Structured prediction is performed by a dense CRF that operates on the optimized features. Experimental results demonstrate that the presented approach increases the accuracy and temporal consistency of semantic video segmentation.
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关键词
dense CRF,structured prediction,Euclidean distance minimization,Euclidean feature space,pixel mapping,long-range spatiotemporal regularization,semantic video segmentation,feature space optimization
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