Abstract
This paper proposes a geodesic-distance-based feature that encodes global information for improved video segmentation algorithms. The feature is a joint histogram of intensity and geodesic distances, where the geodesic distances are computed as the shortest paths between superpixels via their boundaries. We also incorporate adaptive voting weights and spatial pyramid configurations to include spatial information into the geodesic histogram feature and show that this further improves results. The feature is generic and can be used as part of various algorithms. In experiments, we test the geodesic histogram feature by incorporating it into two existing video segmentation frameworks. This leads to significantly better performance in 3D video segmentation benchmarks on two datasets.
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Acknowledgement
Partially supported by the Vietnam Education Foundation, NSF IIS-1161876, FRA DTFR5315C00011, the Stony Brook SensonCAT, the SubSample project from the DIGITEO Institute, France, and a gift from Adobe Corporation
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Le, H., Nguyen, V., Yu, CP., Samaras, D. (2017). Geodesic Distance Histogram Feature for Video Segmentation. In: Lai, SH., Lepetit, V., Nishino, K., Sato, Y. (eds) Computer Vision – ACCV 2016. ACCV 2016. Lecture Notes in Computer Science(), vol 10111. Springer, Cham. https://doi.org/10.1007/978-3-319-54181-5_18
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DOI: https://doi.org/10.1007/978-3-319-54181-5_18
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