On the Assessment of Segmentation Methods for Images of Mosaics

Gianfranco Fenu, Nikita Jain, Eric Medvet, Felice Andrea Pellegrino, Myriam Pilutti Namer

2015

Abstract

The present paper deals with automatic segmentation of mosaics, whose aim is obtaining a digital representation of the mosaic where the shape of each tile is recovered. This is an important step, for instance, for preserving ancient mosaics. By using a ground-truth consisting of a set of manually annotated mosaics, we objectively compare the performance of some existing recent segmentation methods, based on a simple error metric taking into account precision, recall and the error on the number of tiles. Moreover, we introduce some mosaic-specific hardness estimators (namely some indexes of how difficult is the task of segmenting a particular mosaic image). The results show that the only segmentation algorithm specifically designed for mosaics performs better than the general purpose algorithms. However, the problem of segmentation of mosaics appears still partially unresolved and further work is needed for exploiting the specificity of mosaics in designing new segmentation algorithms.

References

  1. Achanta, R., Shaji, A., Smith, K., Lucchi, A., Fua, P., and Susstrunk, S. (2012). Slic superpixels compared to state-of-the-art superpixel methods. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 34(11):2274-2282.
  2. Benyoussef, L. and Derrode, S. (2008). Tessella-oriented segmentation and guidelines estimation of ancient mosaic images. Journal of Electronic Imaging, 17(4):043014-043014.
  3. Benyoussef, L. and Derrode, S. (2011). Analysis of ancient mosaic images for dedicated applications. In Stanco, F., Battiato, S., and Gallo, G., editors, Digital Imaging for Cultural Heritage Preservation: Analysis, Restoration, and Reconstruction of Ancient Artworks. CRC Press.
  4. Levinshtein, A., Stere, A., Kutulakos, K. N., Fleet, D. J., Dickinson, S. J., and Siddiqi, K. (2009). Turbopixels: Fast superpixels using geometric flows. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 31(12):2290-2297.
  5. Liu, M.-Y., Tuzel, O., Ramalingam, S., and Chellappa, R. (2011). Entropy rate superpixel segmentation. In Computer Vision and Pattern Recognition (CVPR), 2011 IEEE Conference on, pages 2097-2104. IEEE.
  6. Moore, A. P., Prince, S., Warrell, J., Mohammed, U., and Jones, G. (2008). Superpixel lattices. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8. IEEE.
  7. Neubert, P. and Protzel, P. (2012). Superpixel benchmark and comparison. In Proc. Forum Bildverarbeitung.
  8. Ren, X. and Malik, J. (2003). Learning a classification model for segmentation. In Computer Vision, 2003.
  9. Proceedings. Ninth IEEE International Conference on, pages 10-17. IEEE.
  10. Vincent, L. and Soille, P. (1991). Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations. IEEE Transactions on Pattern Analysis and Machine Intelligence, 13(6):583-598.
  11. Yasnoff, W. A., Mui, J. K., and Bacus, J. W. (1977). Error measures for scene segmentation. Pattern Recognition, 9(4):217-231.
  12. Zhang, H., Fritts, J. E., and Goldman, S. A. (2008). Image segmentation evaluation: A survey of unsupervised methods. Computer Vision and Image Understanding, 110(2):260-280.
Download


Paper Citation


in Harvard Style

Fenu G., Jain N., Medvet E., Pellegrino F. and Namer M. (2015). On the Assessment of Segmentation Methods for Images of Mosaics . In Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015) ISBN 978-989-758-091-8, pages 130-137. DOI: 10.5220/0005310101300137


in Bibtex Style

@conference{visapp15,
author={Gianfranco Fenu and Nikita Jain and Eric Medvet and Felice Andrea Pellegrino and Myriam Pilutti Namer},
title={On the Assessment of Segmentation Methods for Images of Mosaics},
booktitle={Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)},
year={2015},
pages={130-137},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005310101300137},
isbn={978-989-758-091-8},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 10th International Conference on Computer Vision Theory and Applications - Volume 3: VISAPP, (VISIGRAPP 2015)
TI - On the Assessment of Segmentation Methods for Images of Mosaics
SN - 978-989-758-091-8
AU - Fenu G.
AU - Jain N.
AU - Medvet E.
AU - Pellegrino F.
AU - Namer M.
PY - 2015
SP - 130
EP - 137
DO - 10.5220/0005310101300137