Unsupervised image clustering algorithm based on contrastive learning and K-nearest neighbors

With the development of the times, people generate a huge amount of data every day, most of which are unlabeled data, but manual labeling needs a lot of time and effort, so unsupervised algorithms are being used more often. This paper proposes an unsupervised image clustering algorithm based on contrastive learning and K-nearest neighbors (CLKNN). CLKNN is trained in two steps, which are the representation learning step and the clustering step. Contrastive learning and K-nearest neighbors have a huge impact on CLKNN. In the representation learning step, firstly CLKNN processes the image by double data augmentation to get two different augmented images; then CLKNN uses double contrastive loss to extract the high-level feature information of the augmented images, maximizing the similarity of row space and maximizing the similarity of column space to ensure the invariance of information. In the clustering step, CLKNN finds the nearest neighbors of each image by K-nearest neighbors, then it maximizes the similarity between each image and its nearest neighbors to get the final result. To test the performance of CLKNN, the experiments are conducted on CIFAR-10, CIFAR-100 and STL-10 in this paper. From the final results, it is clear that CLKNN has better performance than other advanced algorithms.