GellineauHuangLee

Introduction

In recent years, advertisers and magazine editors have been accused of over-glamorizing the subjects of their photoshoots. Through photo-editing software like Adobe Photoshop, models have graced ads and magazine covers with seemingly impossibly slim and curvy figures, or impossibly smooth skin. In response to this phenomenon of 'photoshopping to the extreme,' Eric Kee and Hany Farid published a paper in 2011, detailing on using computers and algorithms to judge how much a picture has been altered--something that usually only a human being might be able to perceive properly.

This project is in essence an attempt to follow and replicate the results obtained by Kee and Farid.

Methods

Describe your algorithm or approach. Detail any issues or problems that were particularly important. Emphasize the parts of the project that you wrote (instead of ISET or downloaded code). Describe the analysis in enough detail so that someone could understand and repeat your analysis. What data and software did you use? What were the ideas of the algorithm and data analysis?

Data Collection

A series of approximately 137 before and after images were collected off of the internet. Full body, face and torso masks were individually and manually made for each of the images.

In addition, a website was also constructed, which would allow a user to rate the degree of image manipulation for up to seventy photos , then send that information back to us for the purpose of training our parameters.

Results

Organize your results in a good logical order (not necessarily historical order). Include relevant graphs and/or images. Make sure graph axes are labeled. Make sure you draw the reader's attention to the key element of the figure. The key aspect should be the most visible element of the figure or graph. Help the reader by writing a clear figure caption.

Conclusions

Describe what you learned. What worked? What didn't? Why? What would you do if you kept working on the project?

References

Kee E, Farid H, 2011. A Perceptual Metric for Photo Retouching.

Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines. ACM Transactions on Intelligent Systems and Technology, 2:27:1--27:27, 2011. Software available at [[1]]

Z. Wang, A. C. Bovik, H. R. Sheikh and E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity," IEEE Transactions on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.

Appendix I

Upload source code, some result images, etc, and give a description of each link. In some cases, your acquired data may be too large to store practically. In this case, use your judgement (or consult one of us) and only link the most relevant data. Be sure to describe the purpose of your code and to edit the code for clarity. The purpose of placing the code online is to allow others to verify your methods and to learn from your ideas. It should be possible for someone else to generate result images using your code.

Appendix II

(For groups Only) Work breakdown. Explain how the project work was divided among group members.