  In the face of complex natural images, existing deep clustering algorithms
fall significantly short in terms of clustering accuracy when compared to
supervised classification methods, making them less practical. This paper
introduces an image clustering algorithm based on self-supervised pretrained
models and latent feature distribution optimization, substantially enhancing
clustering performance. It is found that: (1) For complex natural images, we
effectively enhance the discriminative power of latent features by leveraging
self-supervised pretrained models and their fine-tuning, resulting in improved
clustering performance. (2) In the latent feature space, by searching for
k-nearest neighbor images for each training sample and shortening the distance
between the training sample and its nearest neighbor, the discriminative power
of latent features can be further enhanced, and clustering performance can be
improved. (3) In the latent feature space, reducing the distance between sample
features and the nearest predefined cluster centroids can optimize the
distribution of latent features, therefore further improving clustering
performance. Through experiments on multiple datasets, our approach outperforms
the latest clustering algorithms and achieves state-of-the-art clustering
results. When the number of categories in the datasets is small, such as
CIFAR-10 and STL-10, and there are significant differences between categories,
our clustering algorithm has similar accuracy to supervised methods without
using pretrained models, slightly lower than supervised methods using
pre-trained models. The code linked algorithm is
https://github.com/LihengHu/ICBPL.
