Interactive Learning Using a "Society of Models"

By Tom Minka, Rosalind W. Picard. Computer Vision and Pattern Recognition, 2017

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Automatic Linguistic Indexing of Pictures by a Statistical Modeling Approach (2003, background): “Many researchers have attempted to use machine-learning techniques for image indexing and retrieval [16], [24].”
Computing (2021, methodology): “Intervals were collected over a 5min baseline (resting) and throughout the participant’s walk through the study area.”
SIMPLIcity: Semantics-Sensitive Integrated Matching for Picture LIbraries (2000, background): “In the academic domain, MIT Photobook [15], [17], [12] is one of the earliest.”
Blobworld: Image Segmentation Using Expectation-Maximization and Its Application to Image Querying (2002, methodology): “For texture histograms, we discretized the two texture features (mean contrast and anisotropy) into 21 bins each, spaced uniformly on the interval from 0 to 1.”
A Bayesian framework for concept learning (1999, background): “Providing more than a few3For contemporary examples of such a system, see Minka & Picard (1997) and De Bonet & Viola(1997).”
Image classification for content-based indexing (2001, background): “Recent research in human perception of image content [21], [24], [27], [31] suggests the importance of semantic cues for efficient retrieval.”