Abstract
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, con- tour 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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© 1999 Springer-Verlag Berlin Heidelberg
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Malik, J., Shi, J., Belongie, S., Leung, T. (1999). Grouping in the Normalized Cut Framework. In: Shape, Contour and Grouping in Computer Vision. Lecture Notes in Computer Science, vol 1681. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46805-6_9
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DOI: https://doi.org/10.1007/3-540-46805-6_9
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