TY - GEN
T1 - Defuzzification by Feature Distance Minimization Based on DC Programming
AU - Lindblad, Joakim
AU - Sladoje, Natasha
AU - Lukic, Tibor
PY - 2007
Y1 - 2007
N2 - We introduce the use of DC programming, in combination with convex-concave regularization, as a deterministic approach for solving the optimization problem imposed by defuzzification by feature distance minimization. We provide a DC based algorithm for finding a solution to the defuzzification problem by expressing the objective function as a difference of two convex functions and iteratively solving a family of DC programs. We compare the performance with the previously recommended method, simulated annealing, on a number of test images. Encouraging results, together with several advantages of the DC based method, approve use of this approach, and motivate its further exploration
AB - We introduce the use of DC programming, in combination with convex-concave regularization, as a deterministic approach for solving the optimization problem imposed by defuzzification by feature distance minimization. We provide a DC based algorithm for finding a solution to the defuzzification problem by expressing the objective function as a difference of two convex functions and iteratively solving a family of DC programs. We compare the performance with the previously recommended method, simulated annealing, on a number of test images. Encouraging results, together with several advantages of the DC based method, approve use of this approach, and motivate its further exploration
UR - https://res.slu.se/id/publ/17125
U2 - 10.1109/ISPA.2007.4383722
DO - 10.1109/ISPA.2007.4383722
M3 - Conference paper in proceedings
SN - 978-953-184-116-0
T3 - Image And Signal Processing And Analysis
SP - 373
EP - 378
BT - 5th International Symposium on Image and Signal Processing and Analysis
ER -