Monte-Carlo Image Retargeting

Roberto Gallea, Edoardo Ardizzone, Roberto Pirrone


In this paper an efficient method for image retargeting is proposed. It relies on a monte-carlo model that makes use of image saliency. Each random sample is extracted from deformation probability mass function defined properly, and shrinks or enlarges the image by a fixed size. The shape of the function, determining which regions of the image are affected by the deformations, depends on the image saliency. High informative regions are less likely to be chosen, while low saliency regions are more probable. Such a model does not require any optimization, since its solution is obtained by extracting repeatedly random samples, and allows real-time application even for large images. Computation time can be additionally improved using a parallel implementation. The approach is fully automatic, though it can be improved by providing interactively cues such as geometric constraints and/or automatic or manual labeling of relevant objects. The results prove that the presented method achieves results comparable or superior to reference methods, while improving efficiency.


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Paper Citation

in Harvard Style

Gallea R., Ardizzone E. and Pirrone R. (2014). Monte-Carlo Image Retargeting . In Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014) ISBN 978-989-758-003-1, pages 402-408. DOI: 10.5220/0004744404020408

in Bibtex Style

author={Roberto Gallea and Edoardo Ardizzone and Roberto Pirrone},
title={Monte-Carlo Image Retargeting},
booktitle={Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)},

in EndNote Style

JO - Proceedings of the 9th International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2014)
TI - Monte-Carlo Image Retargeting
SN - 978-989-758-003-1
AU - Gallea R.
AU - Ardizzone E.
AU - Pirrone R.
PY - 2014
SP - 402
EP - 408
DO - 10.5220/0004744404020408