DeepTagPhoto: Expert-guided unsupervised clustering of Thai architectural photography using pre-trained CNN models

This study presents an unsupervised image clustering approach to support expert-led analysis and tagging of traditional Thai architectural photography. This study evaluates how effectively deep-feature clustering derived from pre-trained CNN embeddings reveal meaningful groupings within Thai stupa photography without relying on existing labels. A subset of 824 images depicting stupas was selected from an original academic archival 7696-image dataset in order to investigate the feasibility of clustering based on deep features extracted from four pre-trained Convolutional Neural Networks (CNNs): VGG19, ResNet50, DenseNet, and InceptionV3. These embeddings were clustered using the K-means algorithm, and results were validated qualitatively by a domain expert. Among the models, ResNet50 achieved the lowest rejection rate and highest alignment with expert interpretations. Notably, the expert's engagement with the clustering outputs led to the revision of prior annotations, revealing instances of label noise and enhancing intra-cluster specificity. These findings suggest that CNN-based clustering can mitigate the limitations of expert-dependent tagging, namely incompleteness, subjectivity, and inconsistency while offering a generalizable framework for similar heritage datasets with sparse or noisy labels. The study contributes a hybrid human-AI methodology for visual cultural heritage analysis and highlights opportunities in supervised style classification and automated archiving workflows.