Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/606586
Title: Image Retargeting by Texture-Aware Synthesis
Authors: Weiming Dong;Fuzhang Wu;Yan Kong;Xing Mei;Tong-Yee Lee;Xiaopeng Zhang
subject: Natural image|texture detection|texture-based significance map|texture-aware synthesis
Year: 2016
Publisher: IEEE
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.
Description: 
URI: http://localhost/handle/Hannan/137892
http://localhost/handle/Hannan/606586
ISSN: 1077-2626
volume: 22
issue: 2
Appears in Collections:2016

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Title: Image Retargeting by Texture-Aware Synthesis
Authors: Weiming Dong;Fuzhang Wu;Yan Kong;Xing Mei;Tong-Yee Lee;Xiaopeng Zhang
subject: Natural image|texture detection|texture-based significance map|texture-aware synthesis
Year: 2016
Publisher: IEEE
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.
Description: 
URI: http://localhost/handle/Hannan/137892
http://localhost/handle/Hannan/606586
ISSN: 1077-2626
volume: 22
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7117450.pdf16.33 MBAdobe PDFThumbnail
Preview File
Title: Image Retargeting by Texture-Aware Synthesis
Authors: Weiming Dong;Fuzhang Wu;Yan Kong;Xing Mei;Tong-Yee Lee;Xiaopeng Zhang
subject: Natural image|texture detection|texture-based significance map|texture-aware synthesis
Year: 2016
Publisher: IEEE
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.
Description: 
URI: http://localhost/handle/Hannan/137892
http://localhost/handle/Hannan/606586
ISSN: 1077-2626
volume: 22
issue: 2
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7117450.pdf16.33 MBAdobe PDFThumbnail
Preview File