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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.<\/jats:p>","DOI":"10.1007\/s40747-023-01081-9","type":"journal-article","created":{"date-parts":[[2023,5,24]],"date-time":"2023-05-24T05:01:51Z","timestamp":1684904511000},"page":"6541-6551","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pseudo-supervised image clustering based on meta-features"],"prefix":"10.1007","volume":"9","author":[{"given":"Hao","family":"Wang","sequence":"first","affiliation":[]},{"given":"Youjia","family":"Shao","sequence":"additional","affiliation":[]},{"given":"Tongsen","family":"Yang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4420-3825","authenticated-orcid":false,"given":"Wencang","family":"Zhao","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,5,24]]},"reference":[{"key":"1081_CR1","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. 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