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The Multidimensional Wisdom of Crowds
Peter Welinder · Steve Branson · Serge Belongie · Pietro Perona

Wed Dec 08 12:00 AM -- 12:00 AM (PST) @ None #None

Distributing labeling tasks among hundreds or thousands of annotators is an increasingly important method for annotating large datasets. We present a method for estimating the underlying value (e.g. the class) of each image from (noisy) annotations provided by multiple annotators. Our method is based on a model of the image formation and annotation process. Each image has different characteristics that are represented in an abstract Euclidean space. Each annotator is modeled as a multidimensional entity with variables representing competence, expertise and bias. This allows the model to discover and represent groups of annotators that have different sets of skills and knowledge, as well as groups of images that differ qualitatively. We find that our model predicts ground truth labels on both synthetic and real data more accurately than state of the art methods. Experiments also show that our model, starting from a set of binary labels, may discover rich information, such as different ``schools of thought'' amongst the annotators, and can group together images belonging to separate categories.

Author Information

Peter Welinder (Caltech)
Steve Branson (University of California San Diego)
Serge Belongie (Cornell University)
Pietro Perona (California Institute of Technology)

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