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Learning High-Level Judgments of Urban Perception
Vicente Ordonez and Tamara L. Berg
University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USAvicente@cs.unc.edu
tlberg@cs.unc.edu
Abstract. Human observers make a variety of perceptual inferences about pictures of places based on prior knowledge and experience. In this paper we apply computational vision techniques to the task of predicting the perceptual characteristics of places by leveraging recent work on visual features along with a geo-tagged dataset of images associated with crowd-sourced urban perception judgments for wealth, uniqueness, and safety. We perform extensive evaluations of our models, training and testing on images of the same city as well as training and testing on images of different cities to demonstrate generalizability. In addition, we collect a new densely sampled dataset of streetview images for 4 cities and explore joint models to collectively predict perceptual judgments at city scale. Finally, we show that our predictions correlate well with ground truth statistics of wealth and crime.
LNCS 8694, p. 494 ff.
lncs@springer.com
© Springer International Publishing Switzerland 2014