Automated Color Clustering for Medieval Manuscript Analysis

Given a color image of a medieval manuscript page, we propose a simple, yet efficient algorithm for automatically estimating the number of its color-based pixel groups, $K$. We formulate this estimation as a minimization problem, where the objective function assesses the quality of a candidate clustering. Rather than using all the features of the given image, we carefully select a subset of features to perform clustering. The proposed algorithm was extensively evaluated on a dataset of 2198 images (1099 original images and their 1099 variants produced by modifying both spatial and spectral resolutions of the originals) from the Yale's Institute for the Preservation of Cultural Heritage (IPCH). The experimental results show that it is able to yield satisfactory estimates of $K$ for these test images.

@InProceedings{YPGR15,
  author       = {Yang, Y. and Pintus, R. and Gobbetti, E. and Rushmeier, H.},
  title        = {Automated Color Clustering for Medieval Manuscript Analysis},
  booktitle    = {Proc. Digital Heritage - Short Papers},
  year         = {2015},
  note         = {HELIOS: HIGH-FIDELITY EXPLORATION OF ANNOTATED 3D CULTURAL OBJECTS},
  keywords     = {automatic manuscrypt analysis},
  url          = {https://publications.crs4.it/pubdocs/2015/YPGR15},
}