Nguyen Thanh Minh, Wu Q M Jonathan, Ahuja Siddhant
Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B-3P4, Canada.
IEEE Trans Neural Netw. 2010 Aug;21(8):1326-38. doi: 10.1109/TNN.2010.2054109. Epub 2010 Jul 19.
Standard gaussian mixture modeling (GMM) is a well-known method for image segmentation. However, the pixels themselves are considered independent of each other, making the segmentation result sensitive to noise. To reduce the sensitivity of the segmented result with respect to noise, Markov random field (MRF) models provide a powerful way to account for spatial dependences between image pixels. However, their main drawback is that they are computationally expensive to implement, and require large numbers of parameters. Based on these considerations, we propose an extension of the standard GMM for image segmentation, which utilizes a novel approach to incorporate the spatial relationships between neighboring pixels into the standard GMM. The proposed model is easy to implement and compared with MRF models, requires lesser number of parameters. We also propose a new method to estimate the model parameters in order to minimize the higher bound on the data negative log-likelihood, based on the gradient method. Experimental results obtained on noisy synthetic and real world grayscale images demonstrate the robustness, accuracy and effectiveness of the proposed model in image segmentation, as compared to other methods based on standard GMM and MRF models.
标准高斯混合模型（GMM）是一种众所周知的图像分割方法。然而，像素本身被认为是相互独立的，这使得分割结果对噪声敏感。为了降低分割结果对噪声的敏感度，马尔可夫随机场（MRF）模型提供了一种强大的方法来考虑图像像素之间的空间依赖性。然而，它们的主要缺点是实现起来计算成本高昂，并且需要大量参数。基于这些考虑，我们提出了一种用于图像分割的标准GMM扩展方法，该方法采用一种新颖的方式将相邻像素之间的空间关系纳入标准GMM。所提出的模型易于实现，并且与MRF模型相比，所需参数数量更少。我们还提出了一种新的方法来估计模型参数，以便基于梯度法最小化数据负对数似然的上界。在有噪声的合成和真实世界灰度图像上获得的实验结果表明，与基于标准GMM和MRF模型的其他方法相比，所提出的模型在图像分割中具有鲁棒性、准确性和有效性。
