TY - JOUR
T1 - A critical view of context
AU - Wolf, Lior
AU - Bileschi, Stanley
N1 - Funding Information:
This report describes research done at the Center for Biological & Computational Learning, which is in the McGovern Institute for Brain Research at MIT, as well as in the Dept. of Brain & Cognitive Sciences, and which is affiliated with the Computer Sciences & Artificial Intelligence Laboratory (CSAIL). This research was sponsored by grants from: Office of Naval Research (DARPA) Contract No. MDA972-04-1-0037, Office of Naval Research (DARPA) Contract No. N00014-02-1-0915, National Science Foundation-NIH (CRCNS) Contract No. EIA-0218506, and National Institutes of Health (Conte) Contract No. 1 P20 MH66239-01A1. Additional support was provided by: Central Research Institute of Electric Power Industry (CRIEPI), Daimler-Chrysler AG, Eastman Kodak Company, Honda Research Institute USA, Inc., Komatsu Ltd., Merrill-Lynch, NEC Fund, Oxygen, Siemens Corporate Research, Inc., Sony, Sumitomo Metal Industries, Toyota Motor Corporation, and the Eugene McDermott Foundation.
PY - 2006/8
Y1 - 2006/8
N2 - In this study, a discriminative detector for object context is designed and tested. The context-feature is simple to implement, feed-forward, and effective across multiple object types in a street-scenes environment. Using context alone, we demonstrate robust detection of locations likely to contain bicycles, cars, and pedestrians. Furthermore, experiments are conducted so as to address several open questions regarding visual context. Specifically, it is demonstrated that context may be determined from low level visual features (simple color and texture descriptors) sampled over a wide receptive field. At least for the framework tested, high level semantic knowledge, e.g, the nature of the surrounding objects, is superfluous. Finally, it is shown that when the target object is unambiguously visible, context is only marginally useful.
AB - In this study, a discriminative detector for object context is designed and tested. The context-feature is simple to implement, feed-forward, and effective across multiple object types in a street-scenes environment. Using context alone, we demonstrate robust detection of locations likely to contain bicycles, cars, and pedestrians. Furthermore, experiments are conducted so as to address several open questions regarding visual context. Specifically, it is demonstrated that context may be determined from low level visual features (simple color and texture descriptors) sampled over a wide receptive field. At least for the framework tested, high level semantic knowledge, e.g, the nature of the surrounding objects, is superfluous. Finally, it is shown that when the target object is unambiguously visible, context is only marginally useful.
KW - Context
KW - Learning
KW - Object detection
KW - Scene understanding
KW - Streetscenes
UR - http://www.scopus.com/inward/record.url?scp=33744510605&partnerID=8YFLogxK
U2 - 10.1007/s11263-006-7538-0
DO - 10.1007/s11263-006-7538-0
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AN - SCOPUS:33744510605
SN - 0920-5691
VL - 69
SP - 251
EP - 261
JO - International Journal of Computer Vision
JF - International Journal of Computer Vision
IS - 2
ER -