A Minimum Description Length-Based Image Segmentation Procedure, and its Comparison with a Cross-Validation-Based Segmentation Procedure
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基于最小描述长度的图像分割及其与交叉验证方法的比较
- 研究问题
- 如何自动把数字图像划分为互不重叠的同质区域，并比较最小描述长度与交叉验证准则所产生的分割效果？
- 方法与思路
- 作者以分段常数函数拟合图像，构造最小描述长度分割准则，再用快速合并算法尝试寻找该准则的最小值。
- 材料陈述的贡献
- 摘要提出自动分割准则及配套搜索算法，并称模拟结果支持其优于所比较的交叉验证方法，同时讨论可能的扩展。
- 阅读关注点
- 应核对描述长度各项的定义、合并算法能否找到全局最优解，以及模拟中的图像类型、误差指标和比较方法设置。
- 阅读边界
- 仅据版本未注明的 OpenAlex 索引摘要，不能推断刊本变化；所述优势来自模拟，摘要未说明搜索保证或普遍优越性。
原文摘要
Image segmentation is a very important problem in image analysis, as quite often it is a key component of a good practical solution to a real-life imaging problem. It aims to partition a digital image into a set of nonoverlapping homogeneous regions. One approach to segmenting an image is to fit a piecewise constant function to the image and define the segmentation by the discontinuity points of the fitted function. The article's first contribution is to present a new and automatic segmentation procedure which follows this piecewise constant function fitting approach. This procedure is based on Rissanen's minimum description length (MDL) principle and consists of two components: (a) an MDL-based criterion in which the “best” segmentation (i.e., the “best” fitted piecewise constant function) is defined as its minimizer and (b) a fast-merging algorithm that attempts to locate this minimizer. As a second contribution, the new MDL-based procedure is compared with a cross-validation based segmentation procedure. Empirical results from a simulation study suggest the new MDL-based procedure is superior. Some possible extensions of the MDL-based procedure are also described.