Paper Gestalt

Carven von Bearnensquash

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

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%

Related work

• Unique work• Text based – biased to certain terms:

“boosting”, “svm”, “crf”, ignores rich visual information

• No previous work known

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}

Approach

• Adaboost

• Select feature classifierwith lowest error rate, increase weight of mis-classified data

Approach

• Empirical error is bounded by

• More math: Include Maxwell’s equations in the paper

• Equations improvepaper gestalt

Features

• gradient, texture, color and spatial information

• LUV histograms, Histograms of Oriented Gradients and gradient magnitude.

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

Experiments -

• Assuming that rejecting 15% of good papers is acceptable, we can cut bad papers in half

Experiments

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

Experiments – good examples

Experiments – bad examples

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.

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