Grouping in the Normalized Cut Framework

In this paper, we study low-level image segmentation in the normalized cut framework proposed by Shi and Malik (1997). The goal is to partition the image from a big picture point of view. Perceptually significant groups are detected first while small variations and details are treated later. Different image features -- intensity, color, texture, contour continuity, motion and stereo disparity are treated in one uniform framework. We suggest directions for intermediate-level grouping on the output of this low-level segmentation.

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