论文标题
使用粒子竞争与合作进行交互式图像分割的复杂网络构建：一种新方法
Complex Network Construction for Interactive Image Segmentation using Particle Competition and Cooperation: A New Approach
论文作者
论文摘要
在交互式图像分割任务中，粒子竞争与合作（PCC）模型是用复杂的网络馈送的，该网络是根据输入图像构建的。在网络构建阶段，需要一个权重矢量来定义特征集中每个元素的重要性，该功能集由相应像素的颜色和位置信息组成，因此要求专家的干预。本文提出了通过网络构建阶段的修改来消除权重矢量。使用从GrabCut数据集，Pascal VOC数据集和Alpha Matting DataSet中提取的151张图像比较了提出的模型和参考模型。将每个模型应用于每个图像30次，以获得误差平均值。这些仿真与参考模型的错误率为3.14 \％时，错误率仅为0.49 \％。与参考模型相比，所提出的方法还显示了评估图像的多样性的误差变化。
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.
