Paper Info
Title
Learning High-Level Judgments Of Urban Perception
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.
Year
DOI
Venue
2014
10.1007/978-3-319-10599-4_32
COMPUTER VISION - ECCV 2014, PT VI
Keywords
Field
DocType
Perceptual Characteristic,Perceptual Judgment,Scene Recognition,Perceptual Score,Fisher Vector
Generalizability theory,Uniqueness,Computer vision,Computational vision,Fisher vector,Computer science,Ground truth,Artificial intelligence,Perception,Machine learning
Conference
Volume
ISSN
Citations 
8694
0302-9743
32
PageRank 
References 
Authors
1.30
25
2
Name
Order
Citations
PageRank
Vicente Ordonez1141869.65
Tamara L. Berg23221225.32