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Conference Paper

Improved Image Boundaries for Better Video Segmentation

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

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

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

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Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-002A-FD0F-3
Abstract
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.