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A Graph-Based Approach for Contextual Image Segmentation

机译：基于图的上下文图像分割方法

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摘要

Image segmentation is one of the most important tasks in Image Analysis since it allows locating the relevant regions of the images and discarding irrelevant information. Any mistake during this phase may cause serious problems to the subsequent methods of the image-based systems. The segmentation process is usually very complex since most of the images present some kind of noise. In this work, two techniques are combined to deal with such problem: one derived from the graph theory and other from the anisotropic filtering methods, both emphasizing the use of contextual information in order to classify each pixel in the image with higher precision. Given a noisy grayscale image, an anisotropic diffusion filter is applied in order to smooth the interior regions of the image, eliminating noise without loosing much information of boundary areas. After that, a graph is built based on the pixels of the obtained diffused image, linking adjacent nodes (pixels) and considering the capacity of the edges as a function of the filter properties. Then, after applying the Ford-Fulkerson algorithm, the minimum cut of the graph is found (following the min cut-max flow theorem), segmenting the object of interest. The results show that the proposed approach outperforms the traditional and well-referenced Otsu's method.
机译：图像分割是图像分析中最重要的任务之一，因为它可以定位图像的相关区域并丢弃不相关的信息。在此阶段中的任何错误都可能对基于图像的系统的后续方法造成严重的问题。分割过程通常非常复杂，因为大多数图像都呈现某种噪声。在这项工作中，两种技术相结合来解决这个问题：一种是从图论推导的，另一种是从各向异性滤波方法推导的，它们都强调使用上下文信息来对图像中的每个像素进行更高的分类。给定嘈杂的灰度图像，将应用各向异性扩散滤镜以平滑图像的内部区域，从而消除噪声而又不会丢失很多边界区域信息。此后，基于获得的漫射图像的像素，将相邻节点（像素）链接在一起，并根据滤镜属性将边缘的容量考虑在内，从而建立一个图形。然后，在应用Ford-Fulkerson算法之后，找到图形的最小割点（遵循最小割裂最大流量定理），对目标对象进行分割。结果表明，所提出的方法优于传统的和参考良好的大津方法。

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