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Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images

arXiv 2209.12621 ↗First posted 26 Sep 2022cs.CV cs.AI cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 45 of its 88 references that are in the map. First posted on arXiv 26 Sep 2022.

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References · 45

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33 more
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