TY - JOUR
T1 - Picture collage
AU - Liu, Tie
AU - Wang, Jingdong
AU - Sun, Jian
AU - Zheng, Nanning
AU - Tang, Xiaoou
AU - Shum, Heung Yeung
PY - 2009/11
Y1 - 2009/11
N2 - In this paper, we address a novel problem of automatically creating a picture collage from a group of images. Picture collage is a kind of visual image summaryto arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. We formulate the picture collage creation problem in a conditional random field model, which integrates image salience, canvas constraint, natural preference, and user interaction. Each image is represented by a group of weighted rectangles, which indicate the salient regions. Then picture collage is resolved by minimizing the energy, guided by the constraints. A two-step optimization method is proposed. First, a quick initialization algorithm based on the proposed 1-D collage method is presented. Second, a very efficient Markov chain Monte Carlo method is designed for the refined optimization. We also integrate user interaction in the formulation and optimization to obtain an interactive collage reflecting personalized preference. Visual and quantitative experimental evaluations indicate the efficiency of the proposed collage creation technique.
AB - In this paper, we address a novel problem of automatically creating a picture collage from a group of images. Picture collage is a kind of visual image summaryto arrange all input images on a given canvas, allowing overlay, to maximize visible visual information. We formulate the picture collage creation problem in a conditional random field model, which integrates image salience, canvas constraint, natural preference, and user interaction. Each image is represented by a group of weighted rectangles, which indicate the salient regions. Then picture collage is resolved by minimizing the energy, guided by the constraints. A two-step optimization method is proposed. First, a quick initialization algorithm based on the proposed 1-D collage method is presented. Second, a very efficient Markov chain Monte Carlo method is designed for the refined optimization. We also integrate user interaction in the formulation and optimization to obtain an interactive collage reflecting personalized preference. Visual and quantitative experimental evaluations indicate the efficiency of the proposed collage creation technique.
KW - 1-D collage
KW - Interactive collage
KW - Markov chain Monto Carlo (MCMC) optimization
KW - Picture collage
UR - https://www.scopus.com/pages/publications/70350267831
U2 - 10.1109/TMM.2009.2030741
DO - 10.1109/TMM.2009.2030741
M3 - 文章
AN - SCOPUS:70350267831
SN - 1520-9210
VL - 11
SP - 1225
EP - 1239
JO - IEEE Transactions on Multimedia
JF - IEEE Transactions on Multimedia
IS - 7
M1 - 5210167
ER -