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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.