Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images
| Autor: | Li, Qinglin, Qiu, Guoping |
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| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Remote Sens. 2022, 14, 3317 |
| Druh dokumentu: | Working Paper |
| DOI: | 10.3390/rs14143317 |
| Popis: | Image clustering is a very useful technique that is widely applied to various areas, including remote sensing. Recently, visual representations by self-supervised learning have greatly improved the performance of image clustering. To further improve the well-trained clustering models, this paper proposes a novel method by first ranking samples within each cluster based on the confidence in their belonging to the current cluster and then using the ranking to formulate a weighted cross-entropy loss to train the model. For ranking the samples, we developed a method for computing the likelihood of samples belonging to the current clusters based on whether they are situated in densely populated neighborhoods, while for training the model, we give a strategy for weighting the ranked samples. We present extensive experimental results that demonstrate that the new technique can be used to improve the State-of-the-Art image clustering models, achieving accuracy performance gains ranging from $2.1\%$ to $15.9\%$. Performing our method on a variety of datasets from remote sensing, we show that our method can be effectively applied to remote--sensing images. Comment: 39 |
| Databáze: | arXiv |
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