Title | ||
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Efficient Supervised Image Clustering Based on Density Division and Graph Neural Networks |
Abstract | ||
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In recent research, supervised image clustering based on Graph Neural Networks (GNN) connectivity prediction has demonstrated considerable improvements over traditional clustering algorithms. However, existing supervised image clustering algorithms are usually time-consuming and limit their applications. In order to infer the connectivity between image instances, they usually created a subgraph for each image instance. Due to the creation and process of a large number of subgraphs as the input of GNN, the computation overheads are enormous. To address the high computation overhead problem in the GNN connectivity prediction, we present a time-efficient and effective GNN-based supervised clustering framework based on density division namely DDC-GNN. DDC-GNN divides all image instances into high-density parts and low-density parts, and only performs GNN subgraph connectivity prediction on the low-density parts, resulting in a significant reduction in redundant calculations. We test two typical models in the GNN connectivity prediction module in the DDC-GNN framework, which are the graph convolutional networks (GCN)-based model and the graph auto-encoder (GAE)-based model. Meanwhile, adaptive subgraphs are generated to ensure sufficient contextual information extraction for low-density parts instead of the fixed-size subgraphs. According to the experiments on different datasets, DDC-GNN achieves higher accuracy and is almost five times quicker than those without the density division strategy. |
Year | DOI | Venue |
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2022 | 10.3390/rs14153768 | REMOTE SENSING |
Keywords | DocType | Volume |
supervised clustering, face clustering graph neural network, link prediction | Journal | 14 |
Issue | ISSN | Citations |
15 | 2072-4292 | 0 |
PageRank | References | Authors |
0.34 | 0 | 6 |
Name | Order | Citations | PageRank |
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Qingchao Zhao | 1 | 0 | 0.68 |
Long Li | 2 | 0 | 0.34 |
Yan Chu | 3 | 13 | 7.09 |
Zhen Yang | 4 | 45 | 13.51 |
Zhengkui Wang | 5 | 0 | 0.34 |
Wen Shan | 6 | 0 | 0.34 |