Multi-view EM algorithm and its application to color image segmentation

In this paper we propose a new algorithm, Multi-View Expectation and Maximization algorithm (Multi-View EM), to deal with real-world learning problems where there are some natural splits of features. Multi-View EM does feature split as Co-training and Co-EM, two successful semi-supervised learning algorithms in text learning tasks, but it considers multi-view learning problem in the framework of EM algorithm. The Multi-View EM algorithm has these impressing advantages comparing with Co-training and Co-EM: its convergence is theoretically guaranteed; it can deal with multiple views instead of only two views. We utilize it for color image segmentation and discuss the phenomenon that different weights for color view and coordinate view will lead to different segmentation results.