Authors:
Roberto Gallea
;
Edoardo Ardizzone
and
Roberto Pirrone
Affiliation:
Universita’ di Palermo, Italy
Keyword(s):
Image Resizing, Image Retargeting, Monte-Carlo, Visual Saliency.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Computer Vision, Visualization and Computer Graphics
;
Enterprise Information Systems
;
Human and Computer Interaction
;
Human-Computer Interaction
;
Image and Video Analysis
;
Mobile Imaging
;
Visual Attention and Image Saliency
Abstract:
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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