The fuzzy local information c-means (FLICM) algorithm, introduced by Krinidis and
Chatzis (2010), was designed to perform highly accurate segmentation of images contaminated
with high-frequency noise. This algorithm includes an extra additive term to the objective
function of the fuzzy c-means (FCM), called local descriptor fuzzy factor, allowing
the labeling of a pixel to be influenced by its neighbors, thus achieving a filtering
effect. Further on, the authors of FLICM claim that their algorithm does not depend
on any trade-off parameter, which were present in all previous similar approaches.
In this paper we investigate the theoretical foundation of FLICM and reveal some critical
issues. First of all, we show that the iterative optimization algorithm proposed for
the minimization of the FLICM objective function is not suitable for the given problem,
it does not minimize the objective function. Instead of that, FLICM computes an FCM-like
partition using distorted distances, according to the local context of each pixel,
thus performing a job that is similar to the so-called suppressed fuzzy c-means algorithm
existing in the literature. Finally we reveal the presence of a possible trade-off
in the definition of the local descriptor fuzzy term, and the necessity of another
factor to compensate against the size of the considered neighborhood. Such algorithms
can be effective in certain scenarios, which were documented by the authors, but a
deep investigation of the limitations would be beneficial.
