Bayesian weighted K-Means clustering algorithm as applied to cotton trash measurement

Image segmentation is one of most difficult tasks in computer vision. It plays a critical role in object recognition of natural images. Unsupervised classification, or clustering, represents one promising approach for solving the image segmentation problem in typical application environments. The K-Means and Bayesian Learning algorithms are two well-known unsupervised classification methods. The K-Means approach is computationally efficient, but assumes imaging conditions which are unrealistic in many practical applications. While the Bayesian learning technique always produces a theoretically optimal segmentation result, the large, computational burden it requires is often unacceptable for many industrial tasks. A novel clustering algorithm, called Bayesian Weighted K-Means, is proposed in this thesis. Through simplification of the Bayesian learning approach's decision-making process using cluster weights, the new technique is able to provide approximately optimal segmentation results while maintaining the computational efficiency generally associated with the K-means algorithm. The capabilities of this new algorithm are demonstrated using both synthetic images with controlled amounts of noise, and real color images of cotton lint contaminated with non-lint material.