TY - GEN
T1 - A data driven method for feature transformation
AU - Dikmen, Mert
AU - Hoiem, Derek W
AU - Huang, Thomas S
PY - 2012/10/1
Y1 - 2012/10/1
N2 - Most image understanding algorithms begin with the extraction of information thought to be relevant to the particular task. This is commonly known as feature extraction and has, up to this date, been a largely manual process, where a reasonable method is chosen through validation on the experimented dataset. In this work we propose a data driven, local histogram based feature extraction method that reduces the manual intervention during the feature computation process and improves on the performance of widely used gradient histogram based features (e.g., HOG). We demonstrate favorable object detection results against HOG on the Inria Pedestrian[7], Pascal 2007[10] data.
AB - Most image understanding algorithms begin with the extraction of information thought to be relevant to the particular task. This is commonly known as feature extraction and has, up to this date, been a largely manual process, where a reasonable method is chosen through validation on the experimented dataset. In this work we propose a data driven, local histogram based feature extraction method that reduces the manual intervention during the feature computation process and improves on the performance of widely used gradient histogram based features (e.g., HOG). We demonstrate favorable object detection results against HOG on the Inria Pedestrian[7], Pascal 2007[10] data.
UR - http://www.scopus.com/inward/record.url?scp=84866694001&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84866694001&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2012.6248069
DO - 10.1109/CVPR.2012.6248069
M3 - Conference contribution
AN - SCOPUS:84866694001
SN - 9781467312264
T3 - Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
SP - 3314
EP - 3321
BT - 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
T2 - 2012 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2012
Y2 - 16 June 2012 through 21 June 2012
ER -