We propose a new density division clustering approach based on GCN, and our experiments demonstrate that the new approach is both time-efficient and effective.
Graph convolutional network solves the problem of non- euclidean spatial structured data that convolutional neural networks cannot process. Kipf [10] et al.
This article aims to analyze under theory of graphs assumptions, small worlds approach, how the configuration of the global network of relations among board ...
Aug 5, 2022 · We present a time-efficient and effective GNN-based supervised clustering framework based on density division namely DDC-GNN.
To adapt to the distribution of realistic problems, a natural approach is to use Graph Convolutional Networks (GCNs) to enhance features for clustering. However ...
Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks. Language: English; Authors: Zhao, Qingchao1 (AUTHOR) Li, Long1 ...
Such methods first build face graphs by deeming images as vertices and then linking them based on their deep features, which are extracted from a trained ...
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Oct 22, 2024 · Finally, a density-based clustering is used to obtain the final clustering results. In contrast to utilize a shallow graph convolutional network ...
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Oct 28, 2021 · This paper focuses on the following problem: Given a large number of unlabeled face images, group them into individual clusters, and the number of clusters ...
We describe an accurate and scalable method for face clustering. A group of faces is classified based on their prospective identities.