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Pseudo-supervised image clustering based on meta-features
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AI
DOI：10.1007/s40747-023-01081-9.png)
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Stable semantics is a prerequisite for achieving excellent image clustering. However, most current methods suffer from inaccurate class semantic estimation, which limits the clustering performance. For the sake of addressing the issue, we propose a pseudo-supervised clustering framework based on meta-features. First, the framework mines meta-semantic features (i.e., meta-features) of image categories based on instance-level features, which not only preserves instance-level information but also ensures the semantic robustness of meta-features. Ulteriorly, we propagate pseudo-labels to its global neighbor samples with meta-features as the center, which effectively avoids the accumulation of errors caused by the misclassification of samples at the cluster boundary. Finally, we exploit the cross-entropy loss with label smoothing to optimize the pseudo-label optimization network. This optimization method not only achieves a direct mapping from features to stable semantic labels, but also effectively avoids suboptimal solutions caused by multi-level optimization. Extensive experiments demonstrate that our method significantly outperforms twenty-one competing clustering methods on six challenging datasets.
Keyword:
Meta-features
Deep clustering
Contrastive learning
期刊
IF:
4.6
论文数:
2.1K
被引数:
6.6K
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