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| 1 | Graph Convolutional Network Combined with Semantic Feature Guidance for Deep Clustering显示文摘The performances of semisupervised clustering for unlabeled data are often superior to those of unsupervised learning,which indicates that semantic information attached to clusters can significantly improve feature representation capability.In a graph convolutional network(GCN),each node contains information about itself and its neighbors that is beneficial to common and unique features among samples.Combining these findings,we propose a deep clustering method based on GCN and semantic feature guidance(GFDC) in which a deep convolutional network is used as a feature generator,and a GCN with a softmax layer performs clustering assignment.First,the diversity and amount of input information are enhanced to generate highly useful representations for downstream tasks.Subsequently,the topological graph is constructed to express the spatial relationship of features.For a pair of datasets,feature correspondence constraints are used to regularize clustering loss,and clustering outputs are iteratively optimized.Three external evaluation indicators,i.e.,clustering accuracy,normalized mutual information,and the adjusted Rand index,and an internal indicator,i.e., the Davidson-Bouldin index(DBI),are employed to evaluate clustering performances.Experimental results on eight public datasets show that the GFDC algorithm is significantly better than the majority of competitive clustering methods,i.e.,its clustering accuracy is20% higher than the best clustering method on the United States Postal Service dataset.The GFDC algorithm also has the highest accuracy on the smaller Amazon and Caltech datasets.Moreover,DBI indicates the dispersion of cluster distribution and compactness within the cluster. | Junfen Chen Jie Han Xiangjie Meng Yan Li Haifeng Li | 2022 | Tsinghua Science and Technology2022，27，5： | 1 |
| 2 | 图像边缘权重优化的最小生成树分割提取显示文摘针对无监督图像分割方法对噪声敏感而导致图像建模困难、分割结果准确率低等问题,该文提出一种图像边缘权重优化的最小生成树分割提取方法。首先,利用L0梯度最小值平滑处理噪声再结合Otsu优化Canny边缘检测,得到更加准确的边缘信息;其次,重新设计权重函数,采用更加合理的色差空间构建加权图,通过改进分割准则优化物体合并与区分过程;最后,选择不同类型图片进行抗噪性、分割效果实验。实验结果表明:相对于其他算法,该文算法的抗噪性能优秀,分割精度平均提升5.15%,过分割率平均下降32.07%,欠分割率平均下降2.69%。将其运用在实际航空遥感图像的河道湖泊提取中,所得结果相比其他主流算法结构更加完整,无关信息更少,抗噪性能更好。 | 林坚普 王栋 肖智阳 林志贤 张永爱 | 2023 | 电子与信息学报2023，45，4： | 0 |
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