CLASP: Adaptive Spectral Clustering for Unsupervised Per-Image Segmentation
To address unsupervised segmentation of large-scale unlabeled images (e.g., advertisements, social media content), this paper proposes CLASP—a lightweight, training-free, annotation-free, and hyperparameter-free framework. Methodologically, CLASP extracts local features using DINO-ViT, constructs a similarity matrix for adaptive spectral clustering (automatically determining the optimal number of clusters), and refines segment boundaries via feature sharpening and DenseCRF post-processing. Its core contribution is an end-to-end segmentation pipeline that eliminates model training, manual labeling, and hyperparameter tuning, thereby significantly enhancing reproducibility and deployment efficiency. Evaluated on COCO-Stuff and ADE20K, CLASP achieves mIoU and pixel accuracy competitive with state-of-the-art unsupervised methods. These results validate its practical utility and generalizability in real-world applications such as brand safety monitoring and creative asset management.