Bunyak Filiz, Palaniappan Kannappan
Department of Computer Science, University of Missouri-Columbia, Columbia, MO, USA.
Proc IEEE Int Conf Comput Vis. 2009 Sep 29;2009:873-880. doi: 10.1109/iccv.2009.5459320.
Graph partitioning active contours (GPAC) is a recently introduced approach that elegantly embeds the graph-based image segmentation problem within a continuous optimization framework. GPAC can be used within parametric snake-based or implicit level set-based active contour continuous paradigms for image partitioning. However, GPAC similar to many other graph-based approaches has quadratic memory requirements which severely limits the scalability of the algorithm to practical problem domains. An N xN image requires O(N(4)) computation and memory to create and store the full graph of pixel inter-relationships even before the start of the contour optimization process. For example, an 1024x1024 grayscale image needs over one terabyte of memory. Approximations using tile/block-based or superpixel-based multiscale grouping of the pixels reduces this complexity by trading off accuracy. This paper describes a new algorithm that implements the exact GPAC algorithm using a constant memory requirement of a few kilobytes, independent of image size.
图划分活动轮廓模型（GPAC）是一种最近提出的方法，它巧妙地将基于图的图像分割问题嵌入到一个连续优化框架中。GPAC可用于基于参数化蛇形模型或基于隐式水平集的活动轮廓连续范式进行图像分割。然而，与许多其他基于图的方法类似，GPAC具有二次内存需求，这严重限制了该算法在实际问题领域的可扩展性。即使在轮廓优化过程开始之前，一个N×N的图像也需要O(N(4))的计算量和内存来创建和存储像素间关系的完整图。例如，一幅1024×1024的灰度图像需要超过1太字节的内存。使用基于瓦片/块或基于超像素的多尺度像素分组进行近似会以牺牲精度为代价降低这种复杂性。本文描述了一种新算法，该算法使用几千字节的恒定内存需求来实现精确的GPAC算法，与图像大小无关。
