Abstract
Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on exampled-based texture synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategies since they have different natures. We propose to retarget the textural regions by example-based synthesis and non-textural regions by fast multi-operator. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image retargeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
| Original language | English |
|---|---|
| Article number | 7117450 |
| Pages (from-to) | 1088-1101 |
| Number of pages | 14 |
| Journal | IEEE Transactions on Visualization and Computer Graphics |
| Volume | 22 |
| Issue number | 2 |
| DOIs | |
| Publication status | Published - 2016 Feb |
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All Science Journal Classification (ASJC) codes
- Software
- Signal Processing
- Computer Vision and Pattern Recognition
- Computer Graphics and Computer-Aided Design
Cite this
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Image retargeting by texture-aware synthesis. / Dong, Weiming; Wu, Fuzhang; Kong, Yan; Mei, Xing; Lee, Tong Yee; Zhang, Xiaopeng.
In: IEEE Transactions on Visualization and Computer Graphics, Vol. 22, No. 2, 7117450, 02.2016, p. 1088-1101.Research output: Contribution to journal › Article
TY - JOUR
T1 - Image retargeting by texture-aware synthesis
AU - Dong, Weiming
AU - Wu, Fuzhang
AU - Kong, Yan
AU - Mei, Xing
AU - Lee, Tong Yee
AU - Zhang, Xiaopeng
PY - 2016/2
Y1 - 2016/2
N2 - Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on exampled-based texture synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategies since they have different natures. We propose to retarget the textural regions by example-based synthesis and non-textural regions by fast multi-operator. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image retargeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
AB - Real-world images usually contain vivid contents and rich textural details, which will complicate the manipulation on them. In this paper, we design a new framework based on exampled-based texture synthesis to enhance content-aware image retargeting. By detecting the textural regions in an image, the textural image content can be synthesized rather than simply distorted or cropped. This method enables the manipulation of textural & non-textural regions with different strategies since they have different natures. We propose to retarget the textural regions by example-based synthesis and non-textural regions by fast multi-operator. To achieve practical retargeting applications for general images, we develop an automatic and fast texture detection method that can detect multiple disjoint textural regions. We adjust the saliency of the image according to the features of the textural regions. To validate the proposed method, comparisons with state-of-the-art image retargeting techniques and a user study were conducted. Convincing visual results are shown to demonstrate the effectiveness of the proposed method.
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UR - http://www.scopus.com/inward/citedby.url?scp=84978701741&partnerID=8YFLogxK
U2 - 10.1109/TVCG.2015.2440255
DO - 10.1109/TVCG.2015.2440255
M3 - Article
AN - SCOPUS:84978701741
VL - 22
SP - 1088
EP - 1101
JO - IEEE Transactions on Visualization and Computer Graphics
JF - IEEE Transactions on Visualization and Computer Graphics
SN - 1077-2626
IS - 2
M1 - 7117450
ER -