.png){kind=icon}
.png){kind=icon}
Return
Context-based object detection in still images
.png){kind=icon}
PRE
AI
DOI：10.1016/j.imavis.2006.02.024.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We present a novel dual-stage object-detection method. In the first stage, an object detector based on appropriate visual features is used to find object candidates. In the second stage, the object candidates are assigned a confidence value based on local-contextual information. Our context-based method is called COBA, for COntext BAsed object detection. At a given detection rate COBA is able to lower the false-detection rate. Experiments in which frontal human faces are to be detected show that the number of false positives is lowered by a factor 8.7 at a detection rate of 80% when compared to the current high-performance object detectors. Moreover, COBA is capable of flexibly using other new object-detection algorithms as 'plug-ins' in the second stage. Hence, object detection can be straightforwardly improved by our method a soon as new insights emerge and are available in algorithmic form. (c) 2006 Elsevier B.V. All rights reserved.
Keywords:
computer vision
machine learning
object recognition
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
4.2
Papers:
4.0K
Citations:
6.7K
Organization
No organization information available