基于粒子竞争与合作的交互式图像分割复杂网络构造新方法
Complex Network Construction for Interactive Image Segmentation using Particle Competition and Cooperation: A New Approach
摘要：
在交互式图像分割任务中，粒子竞争与合作（PCC）模型是由一个复杂的网络，这是从输入图像构建。在网络构建阶段，需要一个权重向量来定义特征集中每个元素的重要性，该特征集由相应像素的颜色和位置信息组成，因此需要专家的干预。本文提出了消除的权重向量，通过修改在网络建设阶段。在不使用权重向量的情况下，使用从Grabcut数据集、PASCAL VOC数据集和Alpha抠图数据集提取的151幅图像对所提出的模型和参考模型进行了比较。将每个模型应用于每个图像30次以获得误差平均值。这些模拟结果在一个出错率只有0.49\%，当分类像素与建议的模型，而参考模型有一个出错率为3.14\%。与参考模型相比，所提出的方法在评估图像的多样性中也呈现出较小的误差变化。
英文摘要：
In the interactive image segmentation task, the Particle Competition and Cooperation (PCC) model is fed with a complex network, which is built from the input image. In the network construction phase, a weight vector is needed to define the importance of each element in the feature set, which consists of color and location information of the corresponding pixels, thus demanding a specialist's intervention. The present paper proposes the elimination of the weight vector through modifications in the network construction phase. The proposed model and the reference model, without the use of a weight vector, were compared using 151 images extracted from the Grabcut dataset, the PASCAL VOC dataset and the Alpha matting dataset. Each model was applied 30 times to each image to obtain an error average. These simulations resulted in an error rate of only 0.49\% when classifying pixels with the proposed model while the reference model had an error rate of 3.14\%. The proposed method also presented less error variation in the diversity of the evaluated images, when compared to the reference model.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
