Pseudo-supervised image clustering based on meta-features

Abstract

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.