Paper detail

Polynomial time algorithms for bi-criteria, multi-objective and ratio problems in clustering and imaging. Part I: Normalized cut and ratio regions

Partitioning and grouping of similar objects plays a fundamental role in image segmentation and in clustering problems. In such problems a typical goal is to group together similar objects, or pixels in the case of image processing. At the same time another goal is to have each group distinctly dissimilar from the rest and possibly to have the group size fairly large. These goals are often combined as a ratio optimization problem. One example of such problem is the normalized cut problem, another is the ratio regions problem. We devise here the first polynomial time algorithms solving these problems optimally. The algorithms are efficient and combinatorial. This contrasts with the heuristic approaches used in the image segmentation literature that formulate those problems as nonlinear optimization problems, which are then relaxed and solved with spectral techniques in real numbers. These approaches not only fail to deliver an optimal solution, but they are also computationally expensive. The algorithms presented here use as a subroutine a minimum $s,t-cut procedure on a related graph which is of polynomial size. The output consists of the optimal solution to the respective ratio problem, as well as a sequence of nested solution with respect to any relative weighting of the objectives of the numerator and denominator. An extension of the results here to bi-criteria and multi-criteria objective functions is presented in part II.