Improving Image Clustering through Sample Ranking and Its Application to remote--sensing images
This work addresses the need for better clustering accuracy in remote sensing and other image applications, but it is incremental as it builds on existing self-supervised learning models.
The paper tackled the problem of improving image clustering by proposing a method that ranks samples within clusters based on confidence and uses a weighted cross-entropy loss for training, resulting in accuracy gains of 2.1% to 15.9% over state-of-the-art models.
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.