Color object segmentation and tracking using flexible statistical model and level-set

Abstract

This study presents an unsupervised novel algorithm for color image segmentation, object detection and tracking based on unsupervised learning step followed with a post processing step implemented with a variational active contour. Flexible learning method of a finite mixture of bounded generalized Gaussian distributions using the Minimum Message Length (MML) principle is developed to cope with the complexity of color images modeling. We deal here simultaneously with the issues of data-model fitting, determining automatically the optimal number of classes and selecting relevant features. Indeed, a feature selection step based on MML is implemented to eliminate uninformative features and therefore improving the algorithm’s performance. For model’s parameters estimation, the maximum likelihood (ML) was investigated and conducted via expectation maximization (EM) algorithm. The obtained object boundaries in the first step are tracked on each frame of a given sequence using a geometric level-set approach. The implementation has the advantage to help in improving the computational efficiency in high-dimensional spaces. We demonstrate the effectiveness of the developed segmentation method through several experiments. Obtained results reveal that our approach is able to achieve higher precision as compared to several other methods for color image segmentation and object tracking.