Bayesian Matting: The Most Classic and Fundamental Algorithm in Image Matting

In the field of image matting, Bayesian Matting stands as one of the most classical and fundamental algorithms. Its MATLAB implementation excels in performance, delivering superior results through efficient probability distribution modeling and optimized alpha channel calculations. The algorithm employs Bayesian inference to estimate foreground and background colors while calculating alpha mattes, typically involving Gaussian mixture models and maximum a posteriori estimation. However, beyond Bayesian Matting, numerous alternative matting algorithms exist, such as GrabCut algorithm which utilizes iterative graph cuts optimization, and Deep Image Matting leveraging convolutional neural networks for sophisticated alpha prediction. Each algorithm possesses distinct advantages and applications across different scenarios. Therefore, when selecting a matting algorithm, it is crucial to consider specific application requirements and contextual factors. A comprehensive evaluation of algorithmic characteristics—including computational complexity, training data dependencies, and hardware compatibility—ensures optimal results by choosing the most suitable approach for each unique use case.