Dynamic image clustering from projected coordinates of deep similarity learning

Commonly used clustering algorithms typically require user parameters such as the number of clusters to be divided. Density-based algorithms do not have such requirements but are not suitable for high dimensional data. Recent studies have merged the cluster assignment task with deep similarity learning. In this paper, we propose a novel framework to perform dynamic image clustering without prior knowledge of the cluster count. A deep learning model first learns data similarity from scratch, followed by the use of a coordinate learning model to project high dimensional data onto a two-dimensional space. A new clustering algorithm, raster clustering, is proposed to evaluate and classify the projected data. This mechanism can be applied in high dimensional data clustering like image data, and it allows the prediction of unseen data in a consistent way without the need for consolidating with training data.