An Improved K - Mode Algorithm for Facial Expression Image Clustering
Xibin Jia, Jianming Yuan, Yujie Xiao · 2016
When recognizing facial expression sequences by discrete Hidden Markov Model, it is necessary to cluster the image frames into several observation states.Considering the complexity of face images in emotion expressing, we partition the whole face into several sub-regions to do clustering separately.Then the categorical values from clustering results of each subregion are combined to do the further clustering.In the paper, we propose an improve K-mode clustering algorithm called Kfrequency to do the category data clustering.Instead of using a simple 0-1 method to determine the similarity between different samples, the K-frequency statistics the frequency of each categorical values of an image in a given cluster and sums them as the similarity between a sample with this cluster.Experiment results on CK+ database show that the bi-level clustering method with K-frequency algorithm outperforms the Single-layer clustering using K-means and bi-level clustering with K-mode.The improved k-modes algorithm K-frequency is more robust than the original k-mode algorithm in dealing with isolated points.