Content-Aware Video Retargeting by Seam Carving

2017 ◽  
pp. 147-157
Author(s):  
Shrinivas D. Desai ◽  
Mahalaxmi Bhille ◽  
Namrata D. Hiremath
Keyword(s):  
Seam Carving ◽  
Video Retargeting ◽  
Content Aware

Image Seam Carving Based On Content Aware Resizing By Gradient Method

2016 ◽  
pp. 25
Author(s):  
مثيل عماد الدين ◽  
رنا محمد حسن
Keyword(s):  
Gradient Method ◽  
Seam Carving ◽  
Content Aware

Detection of Seam-Carving Image Based on Benford's Law for Forensic Applications

2020 ◽  
pp. 39-48
Author(s):  
Guorui Sheng ◽  
Tiegang Gao
Keyword(s):  
Digital Image ◽  
Experimental Result ◽  
Benford’S Law ◽  
Seam Carving ◽  
Benford's Law ◽  
Dct Coefficients ◽  
Digital Image Forgery ◽  
Markov Features ◽  
Content Aware ◽  
Better Than

Seam-Carving is widely used for content-aware image resizing. To cope with the digital image forgery caused by Seam-Carving, a new detecting algorithm based on Benford's law is presented. The algorithm utilize the probabilities of the first digits of quantized DCT coefficients from individual AC modes to detect Seam-Carving images. The experimental result shows that the performance of proposed method is better than that of the method based on traditional Markov features and other existing methods.

Content-aware broadcast soccer video retargeting using fuzzy logic

2011 ◽  
Vol 47 (12) ◽  
pp. 694 ◽  
Author(s):  
L. Gao ◽  
M. Xu ◽  
S.F. Yan ◽  
M.G. Liu ◽  
C.H. Hou ◽  
...  
Keyword(s):  
Fuzzy Logic ◽  
Video Retargeting ◽  
Soccer Video ◽  
Content Aware

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 Fast Content Aware Video Retargeting

2012 ◽  
Vol 40 (9) ◽  
pp. 11-15 ◽  
Author(s):  
Awadhesh Srivastava ◽  
K. K. Biswas
Keyword(s):  
Video Retargeting ◽  
Content Aware

Video retargeting based on SH equalisation and seam carving

2019 ◽  
Vol 13 (8) ◽  
pp. 1333-1340 ◽  
Author(s):  
Hsi-Chin Hsin
Keyword(s):  
Seam Carving ◽  
Video Retargeting

Large Feature Mining With Ensemble Learning for Image Forgery Detection

2022 ◽  
pp. 119-147
Author(s):  
Qingzhong Liu ◽  
Tze-Li Hsu
Keyword(s):  
Ensemble Learning ◽  
Hot Spot ◽  
Image Forensics ◽  
Detection Methods ◽  
Traditional Learning ◽  
Seam Carving ◽  
Double Compression ◽  
Learning Classifier ◽  
Feature Mining ◽  
Content Aware

The detection of different types of forgery manipulation including seam-carving in JPEG images is a hot spot in image forensics. Seam carving was originally designed for content-aware image resizing. It is also being used for forgery manipulation. It is still very challenging to effectively identify the seam carving forgery under recompression. To address the highly challenging detection problems, this chapter introduces an effective approach with large feature mining. Ensemble learning is used to deal with the high dimensionality and to avoid overfitting that may occur with some traditional learning classifier for the detection. The experimental results validate the efficacy of proposed approach to detecting JPEG double compression and exposing the seam-carving forgery while the JPEG recompression is proceeded at the same quality and a lower quality, which is generally much harder for traditional detection methods. The methodology introduced in this chapter provides a strategy and realistic approach to resolve the highly challenging problems in image forensics.

Sign in / Sign up

Export Citation Format

Share Document