Accumulative energy-based seam carving for image resizing

2018 ◽  
Vol 1 (1) ◽  
pp. 1
Author(s):  
Haifeng Zhang ◽  
Yuzhen Niu ◽  
Yuqing Lin ◽  
Jiawen Lin
Keyword(s):  
Seam Carving ◽  
Image Resizing

scholarly journals Detection of Tampering by Image Resizing Using Local Tchebichef Moments

2019 ◽  
Vol 9 (15) ◽  
pp. 3007
Author(s):  
Dengyong Zhang ◽  
Shanshan Wang ◽  
Jin Wang ◽  
Arun Kumar Sangaiah ◽  
Feng Li ◽  
...  
Keyword(s):  
Texture Classification ◽  
Binary Classification ◽  
Classification Problem ◽  
Seam Carving ◽  
Spatial Correlations ◽  
Image Resizing ◽  
Tchebichef Moments ◽  
Output Code ◽  
Universal Detection ◽  
Multi Class Classification

There are many image resizing techniques, which include scaling, scale-and-stretch, seam carving, and so on. They have their own advantages and are suitable for different application scenarios. Therefore, a universal detection of tampering by image resizing is more practical. By preliminary experiments, we found that no matter which image resizing technique is adopted, it will destroy local texture and spatial correlations among adjacent pixels to some extent. Due to the excellent performance of local Tchebichef moments (LTM) in texture classification, we are motivated to present a detection method of tampering by image resizing using LTM in this paper. The tampered images are obtained by removing the pixels from original images using image resizing (scaling, scale-and-stretch and seam carving). Firstly, the residual is obtained by image pre-processing. Then, the histogram features of LTM are extracted from the residual. Finally, an error-correcting output code strategy is adopted by ensemble learning, which turns a multi-class classification problem into binary classification sub-problems. Experimental results show that the proposed approach can obtain an acceptable detection accuracies for the three content-aware image re-targeting techniques.

Detecting seam carving based image resizing using local binary patterns

2015 ◽  
Vol 55 ◽  
pp. 130-141 ◽  
Author(s):  
Ting Yin ◽  
Gaobo Yang ◽  
Leida Li ◽  
Dengyong Zhang ◽  
Xingming Sun
Keyword(s):  
Local Binary Patterns ◽  
Seam Carving ◽  
Image Resizing

Detection of Content-Aware Image Resizing Using Seam Properties

2013 ◽  
Vol 284-287 ◽  
pp. 3074-3078 ◽  
Author(s):  
Seung Jin Ryu ◽  
Hae Yeoun Lee ◽  
Heung Kyu Lee
Keyword(s):  
Empirical Evidence ◽  
Superior Performance ◽  
Seam Carving ◽  
Large Database ◽  
Image Resizing ◽  
Image Content ◽  
Content Aware

Visually convincing content-aware image resizing, which preserves semantically important image content, has been actively researched in recent years. This paper proposes a resizing detector that reveals the trace of seam carving and seam insertion. To unveil the evidence of seam carving, we exploit energy bias of seam carved images. In addition, the correlation between adjacent pixels is analyzed to estimate the inserted seams. Empirical evidence from a large database of test images demonstrates the superior performance of the proposed detector under a variety of settings.

scholarly journals Improved seam carving for stereo image resizing

2013 ◽  
Vol 2013 (1) ◽  
Author(s):  
Bin Yue ◽  
Chun-ping Hou ◽  
Yuan Zhou
Keyword(s):  
Stereo Image ◽  
Seam Carving ◽  
Image Resizing

Optimized image resizing using flow-guided seam carving and an interactive genetic algorithm

2012 ◽  
Vol 71 (3) ◽  
pp. 1013-1031 ◽  
Author(s):  
Jong-Chul Yoon ◽  
Sun-Young Lee ◽  
In-Kwon Lee ◽  
Henry Kang
Keyword(s):  
Genetic Algorithm ◽  
Seam Carving ◽  
Image Resizing ◽  
Interactive Genetic Algorithm

Interactive image resizing using seam carving and object detection

2013 ◽  
Author(s):  
Tamer Galal Moursi Farag ◽  
Sun Xingming ◽  
Yang Gaobo ◽  
Sedeka Mahmoud El Shamy
Keyword(s):  
Object Detection ◽  
Seam Carving ◽  
Image Resizing

scholarly journals Optimized image resizing using seam carving and scaling

2009 ◽  
Author(s):  
Weiming Dong ◽  
Ning Zhou ◽  
Jean-Claude Paul ◽  
Xiaopeng Zhang
Keyword(s):  
Seam Carving ◽  
Image Resizing
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