A Cooperative Learning-Based Clustering Approach to Lip Segmentation Without Knowing Segment Number

It is usually hard to predetermine the true number of segments in lip segmentation. This paper, therefore, presents a clustering-based approach to lip segmentation without knowing the true segment number. The objective function in the proposed approach is a variant of the partition entropy (PE) and features that the coincident cluster centroids in pattern space can be equivalently substituted by one centroid with the function value unchanged. It is shown that the minimum of the proposed objective function can be reached provided that: 1) the number of positions occupied by cluster centroids in pattern space is equal to the true number of clusters and 2) these positions are coincident with the optimal cluster centroids obtained under PE criterion. In implementation, we first randomly initialize the clusters provided that the number of clusters is greater than or equal to the ground truth. Then, an iterative algorithm is utilized to minimize the proposed objective function. For each iterative step, not only is the winner, i.e., the centroid with the maximum membership degree, updated to adapt to the corresponding input data, but also the other centroids are adjusted with a specific cooperation strength, so that they are each close to the winner. Subsequently, the initial overpartition will be gradually faded out with the redundant centroids superposed over the convergence of the algorithm. Based upon the proposed algorithm, we present a lip segmentation scheme. Empirical studies have shown its efficacy in comparison with the existing methods.