Carven von Bearnensquash 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 classifier with 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 improve paper 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