Please use this identifier to cite or link to this item: https://doi.org/10.1109/TMM.2013.2272919
Title: Image re-attentionizing
Authors: Nguyen, T.V.
Ni, B.
Liu, H. 
Xia, W.
Luo, J.
Kankanhalli, M. 
Yan, S. 
Keywords: Attention retargeting
Visual saliency
Issue Date: 2013
Source: Nguyen, T.V., Ni, B., Liu, H., Xia, W., Luo, J., Kankanhalli, M., Yan, S. (2013). Image re-attentionizing. IEEE Transactions on Multimedia 15 (8) : 1910-1919. ScholarBank@NUS Repository. https://doi.org/10.1109/TMM.2013.2272919
Abstract: In this paper, we propose a computational framework, called Image Re-Attentionizing, to endow the target region in an image with the ability of attracting human visual attention. In particular, the objective is to recolor the target patches by color transfer with naturalness and smoothness preserved yet visual attention augmented. We propose to approach this objective within the Markov Random Field (MRF) framework and an extended graph cuts method is developed to pursue the solution. The input image is first over-segmented into patches, and the patches within the target region as well as their neighbors are used to construct the consistency graphs. Within the MRF framework, the unitary potentials are defined to encourage each target patch to match the patches with similar shapes and textures from a large salient patch database, each of which corresponds to a high-saliency region in one image, while the spatial and color coherence is reinforced as pairwise potentials. We evaluate the proposed method on the direct human fixation data. The results demonstrate that the target region(s) successfully attract human attention and in the meantime both spatial and color coherence is well preserved. © 2013 IEEE.
Source Title: IEEE Transactions on Multimedia
URI: http://scholarbank.nus.edu.sg/handle/10635/56250
ISSN: 15209210
DOI: 10.1109/TMM.2013.2272919
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