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Selectively guiding visual concept discovery

Labeling data to train visual concept classifiers requires significant human effort. Active learning addresses labeling overhead by selecting a meaningful subset of data, but often these approaches assume that the set of visual concepts is known in advance. Clustering approaches perform bottom-up discovery of concepts, and reduce labeling effort by moving from instance-based to group-based labeling. Unfortunately, clustering techniques assume a one-to-one mapping between clusters and visual concepts even though learned groups are often not coherent and fail to represent all concepts. We introduce Selective Guidance, a technique that hierarchically clusters data and selectively queries labels of coherent clusters representing different visual concepts. Unlike most active learning and clustering techniques, Selective Guidance does not require any a priori knowledge. Using benchmark data sets we show that Selective Guidance achieves classification accuracy better than active learning and clustering approaches with fewer labeling queries.