Mr. Yin Qing Profile

Yin Qing

at Peking Univ Shenzhen Graduate School

SPIE Involvement:

Author

Publications (3)

This will count as one of your downloads.

You will have access to both the presentation and article (if available).

DOWNLOAD NOW

This content is available for download via your institution's subscription. To access this item, please sign in to your personal account.

Email or Username Forgot your username?

Password Forgot your password?

Show

Keep me signed in

No SPIE account? Create an account

Proceedings Article | 21 July 2017 Paper

A high capacity reversible watermarking scheme for relational databases based on mapping difference expansion

Yin Qing, Yuesheng Zhu, Fan Yang, Yifeng Jiang

Proceedings Volume 10420, 104203L (2017) https://doi.org/10.1117/12.2281527

KEYWORDS: Digital watermarking, Databases

Read Abstract +

The watermarking technique can be used to protect the copyright of relational databases by hiding the ownership information into the relational databases. Difference expansion (DE) technique is one of the common reversible watermarking techniques for numerical relational databases. However, most previous schemes based on DE suffer the problem of low embedding capacity when the difference values between different attributes are relatively large. In this paper, we propose a novel reversible watermarking scheme to solve the above problem. In the scheme, a mapping difference expansion (MDE) method is proposed to convert the differences between attributes to small mapping differences. Based on the MDE, an attribute and tuple selection algorithm is designed to select the suitable data for watermarking, which can increase embedding capacity and reduce distortion. In addition, the majority voting technique is utilized to enhance the robustness of watermarking with the high embedding capacity. The experimental results have shown that the proposed scheme can provide higher embedding capacity, lower distortion and stronger robustness than other schemes.

Proceedings Article | 21 July 2017 Paper

Depth estimation from multi-scale SLIC superpixels using non-parametric learning

Yifeng Jiang, Yuesheng Zhu, Yin Qing, Fan Yang

Proceedings Volume 10420, 1042047 (2017) https://doi.org/10.1117/12.2281579

Read Abstract +

This study introduces a novel depth estimation method that can automatically generate plausible depth map from a single image with unstructured environment. Our goal is to extrapolate depth map with more correct, rich, and distinct depth order, which is both quantitatively accurate as well as visually pleasing. Based on the preexisting DepthTransfer algorithm, our approach primarily transfers depth information at the level of superpixels from the most photometrically similar retrieval images under the framework of non-parametric learning. Posteriorly, we propose to concurrently warp the corresponding superpixels in multi-scale levels, where we employ an improved SLIC technique to segment the RGBD images from coarse to fine. Then, modified Cross Bilateral Filter is leveraged to refine the final depth field. With respect to training and evaluation, we perform our experiment on the popular Make3D dataset and demonstrate that our method outperforms the state-of-the-art in both efficacy and computational efficiency. Especially, the final results show that in qualitatively evaluation, our results are visually superior in realism and simultaneously more immersive.

Proceedings Article | 21 July 2017 Paper

A text zero-watermarking method based on keyword dense interval

Fan Yang, Yuesheng Zhu, Yifeng Jiang, Yin Qing

Proceedings Volume 10420, 104203K (2017) https://doi.org/10.1117/12.2281526

KEYWORDS: Digital watermarking, Feature extraction

Read Abstract +

Digital watermarking has been recognized as a useful technology for the copyright protection and authentication of digital information. However, rarely did the former methods focus on the key content of digital carrier. The idea based on the protection of key content is more targeted and can be considered in different digital information, including text, image and video. In this paper, we use text as research object and a text zero-watermarking method which uses keyword dense interval (KDI) as the key content is proposed. First, we construct zero-watermarking model by introducing the concept of KDI and giving the method of KDI extraction. Second, we design detection model which includes secondary generation of zero-watermark and the similarity computing method of keyword distribution. Besides, experiments are carried out, and the results show that the proposed method gives better performance than other available methods especially in the attacks of sentence transformation and synonyms substitution.

My Library

You currently do not have any folders to save your paper to! Create a new folder below.

Folder Name

Folder Description

View contact details

UPDATE YOUR PROFILE

Is this your profile? Update it now.

Sign into your SPIE.org account

Don’t have a profile and want one?

Create an account on SPIE.org

Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks. You are receiving this notice because your organization may not have SPIE eBooks access.*

*Shibboleth/Open Athens users─please sign in to access your institution's subscriptions.

To obtain this item, you may purchase the complete book in print or electronic format on SPIE.org.

ORGANIZATIONAL
Sign in with credentials provided by your organization.

Organizational Username

Organizational Password

Show/Hide Password

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members:

Non-members: ADD TO CART

Keywords/Phrases

Search In:

Publication Years