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Improved Image Boundaries for Better Video Segmentation

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons79309

Khoreva,  Anna
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons79212

Benenson,  Rodrigo
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons45383

Schiele,  Bernt
Computer Vision and Multimodal Computing, MPI for Informatics, Max Planck Society;

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Zitation

Khoreva, A., Benenson, R., Galasso, F., Hein, M., & Schiele, B. (2016). Improved Image Boundaries for Better Video Segmentation. In G. Hua, & H. Jégou (Eds.), Computer Vision -- ECCV 2016 Workshops (pp. 773-788). Berlin: Springer. doi:10.1007/978-3-319-49409-8_64.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-002A-FD0F-3
Zusammenfassung
Graph-based video segmentation methods rely on superpixels as starting point. While most previous work has focused on the construction of the graph edges and weights as well as solving the graph partitioning problem, this paper focuses on better superpixels for video segmentation. We demonstrate by a comparative analysis that superpixels extracted from boundaries perform best, and show that boundary estimation can be significantly improved via image and time domain cues. With superpixels generated from our better boundaries we observe consistent improvement for two video segmentation methods in two different datasets.