Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1109/CVPR.2012.6248069 | Computer Vision and Pattern Recognition |
Keywords | Field | DocType |
feature extraction,gradient methods,image enhancement,data driven method,feature transformation,gradient histogram based features,image understanding algorithms,local histogram based feature extraction method,manual intervention,object detection | Computer vision,Object detection,Feature transformation,Histogram,Data-driven,Pattern recognition,Feature (computer vision),Computer science,Feature extraction,Artificial intelligence,Cluster analysis,Computation | Conference |
Volume | Issue | ISSN |
2012 | 1 | 1063-6919 E-ISBN : 978-1-4673-1227-1 |
ISBN | Citations | PageRank |
978-1-4673-1227-1 | 13 | 5.65 |
References | Authors | |
22 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| mert dikmen | 1 | 19 | 6.58 |
| Derek Hoiem | 2 | 4998 | 302.66 |
| Thomas S. Huang | 3 | 27815 | 2618.42 |