An Incremental EM Algorithm Based Visual Perceptual Grouping

Abstract

With the increasing demand for analytical calculation for datasets of different scales and types, a diversity of clustering algorithms have been developed in recent years, among which the EM clustering algorithm has a good clustering effect and has remained popular in likelihood applications. Based on the superiority of the EM algorithm on clustering small-scale datasets and to further test the performance of the BIRCH tree in classification application, in this chapter, an integration-based fast incremental EM clustering algorithm is presented for multiple-percept detection tasks in image sequences for mobile robotic applications in an unknown environment. Basically, the proposed algorithm first applies the standard EM algorithm to group image-patch-based feature vectors extracted from an image perceptually. Then, a fast tree-based approximate nearest neighbor classifier is employed to integrate the clustering results. Experiments performed in an outdoor environment demonstrate the efficacy of the proposed method.