Abstract
Color segmentation is an essential problem in image processing. While most of the recent works focus on the segmentation of individual images, we propose to use the temporal color redundancy to segment arbitrary videos. In an initial phase, a k-medoids clustering is applied on histogram peaks observed on few frames to learn the dominant colors composing the recorded scene. In a second phase, these dominant colors are used as reference colors to speed up a color-based segmentation process and, are updated on-the-fly when the scene changes. Our evaluation first shows that the proprieties of k-medoids clustering make it well suited to learn the dominant colors. Then, the efficiency and the effectiveness of the proposed method are demonstrated and compared to standard segmentation benchmarks. This assessment reveals that our approach is more than 250 times faster than the conventional mean-shift segmentation, while preserving the segmentation accuracy.
The original version of this chapter was revised: The copyright line was incorrect. This has been corrected. The Erratum to this chapter is available at DOI: 10.1007/978-3-319-02895-8_64
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Verleysen, C., De Vleeschouwer, C. (2013). Learning and Propagation of Dominant Colors for Fast Video Segmentation. In: Blanc-Talon, J., Kasinski, A., Philips, W., Popescu, D., Scheunders, P. (eds) Advanced Concepts for Intelligent Vision Systems. ACIVS 2013. Lecture Notes in Computer Science, vol 8192. Springer, Cham. https://doi.org/10.1007/978-3-319-02895-8_59
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DOI: https://doi.org/10.1007/978-3-319-02895-8_59
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