论文标题
使用动态和迭代跨越森林的超级像素分割
Superpixel Segmentation using Dynamic and Iterative Spanning Forest
论文作者
论文摘要
作为图像对象的组成部分，Superpixels可以改善几个高级操作。但是，图像分割方法可能会严重损害其准确性，以减少超级像素的数量。我们已经研究了基于迭代跨越森林（ISF）框架的解决方案。在这项工作中，我们提出了动态ISF（DISF） - 一种基于以下步骤的方法。 （a）它是从图像图开始的，与所需数量的Superpixels相比，具有更大像素的种子集。 （b）种子在彼此之间竞争，每个种子都征服了其最紧密的像素，从而与连接的超像素产生了图像分区（跨越森林）。在步骤（c）中，DISF基于超级像素分析将相关值分配给种子，并删除最无关紧要的值。重复步骤（b）和（c），直到达到所需的超级像素数为止。与区域合并算法相比，DISF有机会在每次迭代后重建相关边缘。与其他基于种子的超像素方法相比，DISF更有可能找到相关的种子。它还在ISF框架中引入了动态电弧重量估计，以进行更有效的超像素描述，我们在具有不同对象属性的三个数据集上演示了所有结果。
As constituent parts of image objects, superpixels can improve several higher-level operations. However, image segmentation methods might have their accuracy seriously compromised for reduced numbers of superpixels. We have investigated a solution based on the Iterative Spanning Forest (ISF) framework. In this work, we present Dynamic ISF (DISF) -- a method based on the following steps. (a) It starts from an image graph and a seed set with considerably more pixels than the desired number of superpixels. (b) The seeds compete among themselves, and each seed conquers its most closely connected pixels, resulting in an image partition (spanning forest) with connected superpixels. In step (c), DISF assigns relevance values to seeds based on superpixel analysis and removes the most irrelevant ones. Steps (b) and (c) are repeated until the desired number of superpixels is reached. DISF has the chance to reconstruct relevant edges after each iteration, when compared to region merging algorithms. As compared to other seed-based superpixel methods, DISF is more likely to find relevant seeds. It also introduces dynamic arc-weight estimation in the ISF framework for more effective superpixel delineation, and we demonstrate all results on three datasets with distinct object properties.
