Abstract
This paper proposes a new multi-scale energy minimization algorithm which can be used to efficiently solve large scale labelling problems in computer vision. The basic modus operandi of any multi-scale method involves the construction of a smaller problem which can be solved efficiently. The solution of this problem is used to obtain a partial labelling of the original energy function, which in turn allows us to minimize it by solving its (much smaller) projection. We propose the use of new techniques for both the construction of the smaller problem, and the extraction of a partial solution. Experiments on image segmentation show that our techniques give solutions with low pixel labelling error and in the same or less amount of computation time, compared to traditional multi-scale techniques.
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Kohli, P., Lempitsky, V., Rother, C. (2010). Uncertainty Driven Multi-scale Optimization. In: Goesele, M., Roth, S., Kuijper, A., Schiele, B., Schindler, K. (eds) Pattern Recognition. DAGM 2010. Lecture Notes in Computer Science, vol 6376. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15986-2_25
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DOI: https://doi.org/10.1007/978-3-642-15986-2_25
Publisher Name: Springer, Berlin, Heidelberg
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