Basic level scene understanding: categories, attributes and structures
Name
fpsyg-04-00506.pdf
Size
2.2 MB
Format
Adobe PDF
Checksum (MD5)
89300da5cab383c876fe47b7d8466cf5
Author(s)
Patterson, Genevieve
Xiao, Jianxiong
Hays, James
Russell, Bryan Christopher
Ehinger, Krista A
Torralba, Antonio
Oliva, Aude
Date Issued
August 2013
Journal
Frontiers in Psychology
Publisher
Frontiers Media SA
Citation
Xiao, Jianxiong, James Hays, Bryan C. Russell, Genevieve Patterson, Krista A. Ehinger, Antonio Torralba, and Aude Oliva. “Basic Level Scene Understanding: Categories, Attributes and Structures.” Frontiers in Psychology 4 (2013).
Version
Final published version
Abstract
A longstanding goal of computer vision is to build a system that can automatically understand a 3D scene from a single image. This requires extracting semantic concepts and 3D information from 2D images which can depict an enormous variety of environments that comprise our visual world. This paper summarizes our recent efforts toward these goals. First, we describe the richly annotated SUN database which is a collection of annotated images spanning 908 different scene categories with object, attribute, and geometric labels for many scenes. This database allows us to systematically study the space of scenes and to establish a benchmark for scene and object recognition. We augment the categorical SUN database with 102 scene attributes for every image and explore attribute recognition. Finally, we present an integrated system to extract the 3D structure of the scene and objects depicted in an image.
Subjects
SUN database, basic level scene understanding, scene recognition, scene attributes, geometry recognition, 3D context
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Brain and Cognitive Sciences
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Creative Commons Attribution 4.0 International License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.3389/FPSYG.2013.00506
