1. Carven von Bearnensquash
Paper Gestalt
Carven von Bearnensquash

2. Background Peer review  imperfect review process
Growth in the volume of submissions, tripled over the last 10 years
Less than ideal pool of reviewers
General layout of a paper

3. Abstract Intuition: Quality of paper  general layout of the paper
Computer vision techniques to predict if the paper should be accepted
Result: reject 15% of good papers, cut down the number of “bad papers” by more than 50%

4. Related work Unique work
Text based – biased to certain terms: “boosting”, “svm”, “crf”, ignores rich visual information
No previous work known

5. Approach Formulated as a binary classification task
Training data set of example-label pairs, {(x1; y1); (x2; y2); ...(xn; yn)}, Xi: feature values for paper i, Yi: binary label, “good” or “bad”
Goal: learn a function f: X  {0, 1}

6. Approach
Adaboost
Select feature classifier with lowest error rate, increase weight of mis- classified data

7. Approach Empirical error is bounded by
More math: Include Maxwell’s equations in the paper
Equations improve paper gestalt

8. Features gradient, texture, color and spatial information
LUV histograms, Histograms of Oriented Gradients and gradient magnitude.

9. Experiments - Data Acquisition
Accepted papers from CVPR 2008, ICCV 2009, and CVPR 2009 as positive examples #1196
Workshop papers from these same conferences as an approximation as negative examples #665
Papers converted to images, resized and padded with blank pages.
25% testing and 75% training

10. Experiments -
Assuming that rejecting 15% of good papers is acceptable, we can cut bad papers in half

11. Experiments “we’re not sure what this figure reveals”
bar plots are particularly aesthetically pleasing

12. Experiments – good examples

13. Experiments – bad examples

14. Experiments – the paper itself
The system reported a posterior probability of 88.4%, which reassured us that this paper is fit for the CVPR conference.

15. Conclusions
The quality of a computer vision paper can be estimated well by basic visual features
A real-time system to predict weather a paper should be accepted or rejected