Bioinformatics Institute, Singapore.
IEEE Trans Image Process. 2012 Jun;21(6):2955-68. doi: 10.1109/TIP.2012.2187670. Epub 2012 Feb 13.
In this paper, we consider the problem of segmentation of large collections of images. We propose a semisupervised optimization model that determines an efficient segmentation of many input images. The advantages of the model are twofold. First, the segmentation is highly controllable by the user so that the user can easily specify what he/she wants. This is done by allowing the user to provide, either offline or interactively, some (fully or partially) labeled pixels in images as strong priors for the model. Second, the model requires only minimal tuning of model parameters during the initial stage. Once initial tuning is done, the setup can be used to automatically segment a large collection of images that are distinct but share similar features. We will show the mathematical properties of the model such as existence and uniqueness of solution and establish a maximum/minimum principle for the solution of the model. Extensive experiments on various collections of biological images suggest that the proposed model is effective for segmentation and is computationally efficient.
在本文中，我们考虑了对大型图像集进行分割的问题。我们提出了一种半监督的优化模型，该模型可以确定许多输入图像的有效分割。该模型的优点有两个方面。首先，分割是高度可控的，用户可以很容易地指定他/她想要什么。这是通过允许用户离线或交互地在图像中提供一些（完全或部分）标记的像素作为模型的强先验来实现的。其次，该模型在初始阶段只需要最小化模型参数的调整。一旦初始调整完成，就可以使用该设置自动分割大量不同但具有相似特征的图像。我们将展示模型的数学性质，如解的存在性和唯一性，并为模型的解建立一个最大/最小原理。在各种生物图像集合上的大量实验表明，所提出的模型对于分割是有效的，并且计算效率高。
