CLIP-Guided Clustering with Archetype-Based Similarity and Hybrid Segmentation for Robust Indoor Scene Classification

Accurate classification of indoor scenes remains a challenging problem in computer vision, particularly when datasets contain diverse room types and varying levels of contamination. We propose a novel method, CLIP-Guided Clustering, which introduces archetype-based similarity as a semantic feature space. Instead of directly using raw image embeddings, we compute similarity scores between each image and predefined textual archetypes (e.g., “clean room,” “cluttered room with dry debris,” “moldy bathroom,” “room with workers”). These scores form low-dimensional semantic vectors that enable interpretable clustering via K-Means. To evaluate clustering robustness, we systematically explored UMAP parameter configurations (n_neighbors, min_dist) and identified the optimal setting (n_neighbors = 5, min_dist = 0.0) with the highest silhouette score (0.631). This objective analysis confirms that archetype-based representations improve separability compared with conventional visual embeddings. In addition, we developed a hybrid segmentation pipeline combining the Segment Anything Model (SAM), DeepLabV3, and pre-processing techniques to accurately extract floor regions even in low-quality or cluttered images. Together, these methods provide a principled framework for semantic classification and segmentation of residential environments. Beyond application-specific domains, our results demonstrate that combining vision–language models with segmentation networks offers a generalizable strategy for interpretable and robust scene understanding.