Abstract
We apply deterministic optimization based on Spectral Projected Gradient method in combination with concave regularization to solve the minimization problem imposed by defuzzification by feature distance minimization. We compare the performance of the proposed algorithm with the methods previously recommended for the same task, (non-deterministic) simulated annealing and (deterministic) DC based algorithm. The evaluation, including numerical tests performed on synthetic and real images, shows advantages of the new method in terms of speed and flexibility regarding inclusion of additional features in defuzzification. Its relatively low memory requirements allow the application of the suggested method for defuzzification of 3D objects.
The first and the second author acknowledge the Ministry of Science of the Republic of Serbia for support through the Projects ON144018 and ON144029.
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Lukić, T., Sladoje, N., Lindblad, J. (2008). Deterministic Defuzzification Based on Spectral Projected Gradient Optimization. In: Rigoll, G. (eds) Pattern Recognition. DAGM 2008. Lecture Notes in Computer Science, vol 5096. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69321-5_48
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DOI: https://doi.org/10.1007/978-3-540-69321-5_48
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