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Author Ordonez, Vicente ♦ Berg, Tamara L.
Source CiteSeerX
Content type Text
File Format PDF
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Urban Perception ♦ High-level Judgment ♦ Human Observer ♦ Perceptual Characteristic ♦ Perceptual Judgment ♦ Computational Vision Technique ♦ Prior Knowledge ♦ Joint Model ♦ Crowd-sourced Urban Perception Judgment ♦ Perceptual Inference ♦ Ground Truth Statistic ♦ City Scale ♦ Visual Feature ♦ Streetview Image ♦ Different City ♦ Extensive Evaluation ♦ Geo-tagged Dataset ♦ Recent Work
Abstract 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 pre-dicting 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 im-ages 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. 1
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study
Learning Resource Type Article