Strong image segmentation is a very challenging problem in computer vision research. Both data-driven and model-driven approaches have been investigated in the past two decades, and many approaches proposed. Although model-based approaches are more promising in addressing strong image segmentation, data-driven approaches present more general frameworks which could potentially be adopted to segment general scenes without any prior model information. We discuss the problems of strong image segmentation from a data-driven perspective, and present a modeling technique describing an object with both its segments and a hierarchical relationship among the segments. The paper is devoted to the discussion of the feasibility of data-driven approaches for strong image segmentation. Existing approaches are not suitable for strong image segmentation in complex environments, but preliminary experimental results show the feasibility of our proposed model.
Strong image segmentation from a data-driven perspective: impossible?
2004-01-01
450527 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
NTRS | 2017
|Emerald Group Publishing | 2005
Online Contents | 2009
Online Contents | 1996
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