Paper Info
Title
Image Re-Attentionizing
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.
Year
DOI
Venue
2013
10.1109/TMM.2013.2272919
IEEE Transactions on Multimedia
Keywords
Field
DocType
Visualization,Image color analysis,Coherence,Computational modeling,Educational institutions,Electronic mail,Psychology
Cut,Computer vision,Markov process,Similarity (geometry),Pattern recognition,Computer science,Markov random field,Image texture,Binary image,Coherence (physics),Image segmentation,Artificial intelligence
Journal
Volume
Issue
ISSN
15
8
1520-9210
Citations 
PageRank 
References 
8
0.53
20
Authors
7
Name
Order
Citations
PageRank
Tam V. Nguyen1442.87
Bingbing Ni2142182.90
Hairong Liu337417.41
Wei Xia482.89
Jiebo Luo56314374.00
Mohan Kankanhalli63825299.56
Shuicheng Yan79701359.54