  High-quality labeled datasets are essential for deep learning. Traditional
manual annotation methods are not only costly and inefficient but also pose
challenges in specialized domains where expert knowledge is needed.
Self-supervised methods, despite leveraging unlabeled data for feature
extraction, still require hundreds or thousands of labeled instances to guide
the model for effective specialized image classification. Current unsupervised
learning methods offer automatic classification without prior annotation but
often compromise on accuracy. As a result, efficiently procuring high-quality
labeled datasets remains a pressing challenge for specialized domain images
devoid of annotated data. Addressing this, an unsupervised classification
method with three key ideas is introduced: 1) dual-step feature dimensionality
reduction using a pre-trained model and manifold learning, 2) a voting
mechanism from multiple clustering algorithms, and 3) post-hoc instead of prior
manual annotation. This approach outperforms supervised methods in
classification accuracy, as demonstrated with fungal image data, achieving
94.1% and 96.7% on public and private datasets respectively. The proposed
unsupervised classification method reduces dependency on pre-annotated
datasets, enabling a closed-loop for data classification. The simplicity and
ease of use of this method will also bring convenience to researchers in
various fields in building datasets, promoting AI applications for images in
specialized domains.
