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

Our methodology was to gather statistics that describe the alterations people notice most and use them to predict user perception of a photo manipulation. These statistics are separated into two methods as described in the source paper of this project, geometric distortions and photometric distortions. Geometric distortions describe modifications such as flattening the stomach enlarging the bust and shaping hips which help form our opinion on the degree of modifications. The other most common distortions are classified as photometric distortions. These describe pixel to pixel differences between images and the effect of blurring and sharpening on images. We gather these statistics and compare them to data collected from real users. In this way, we can train the software to not only collect these statistics but produce a rating that agrees with our perception of photo manipulation.

Data Collection

A series of approximately 137 before and after images were collected off of the internet from various sources including: magazine covers, artist demonstrations and collections on discussion forums. Full body, face and torso masks were individually and manually made for each of these images. These were necessary in order to collect relevant statistics. If an object is added to the after photo that doesn't affect our perception we would only be introducing noise by attempting to fit statistics to it. Therefore, fitting blurring filters are only done over the face and geometric distortions are weighted based on where they occur on the body. This step of image preparation is the only non automatic part of the software and was a problem for our source paper as well.

To collect all of the user data a website was also constructed. This website allowed a user to rate the degree of image manipulation on a scale from 1 to 5 for 70 photos chosen at random from our set of 137. We then used Google's form interface to have that information sent back to us for aggregation.

The data collected is summarized below in the figure of the standard error:

Photometric (draft)

Besides geometric adjustment, photographers also edit the photometric features to make the images more stunning. For photometric model, we are focusing on face images, which is also the area catch most attentions. There are many tricks we could do for the face images. For example, utilizing sharpness filter to eye region to make eyes more sparkling, and also applying blurring filter to make skin more smooth(as shown in the following image left:original image, right:altered image).

These linear filters would be used in different regions for different purpose(smoothing and sharpening), so we have to divide the images into smaller patches to analyzing local regions. In the ____'s paper, they used two measuring method to evaluate the degree a face image has been altered: structural similarity(SSIM) and perceptual distortion. For both methods, we use luminance channel, which is easier for us to analyze image contrast.

Structural Similarity (SSIM)

SSIM is a methode to measure how similar two images is, which is including structural and photometric difference.

Perceptual Distortion

Training

The eight parameters obtained from the geometric and photometric analysis were trained on the labels collected from user input. Although Kee and Farid only collected data for regression (that is, the average of all responses for an image), we also calculated labels for the purpose of classification (using the mode of all responses for an image).

The LIBSVM software, developed by Chih-Chung Chang and Chih-Jen Lin at the National Taiwan University was used to train, cross-validate and eventually test our metric.

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

1. Kee E, Farid H, 2011. A Perceptual Metric for Photo Retouching.
2. 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. [1]
3. 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.
4. Senthil Periaswamy, Hany Farid, Medical image registration with partial data, Medical Image Analysis, Volume 10, Issue 3, June 2006, Pages 452-464

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.