Contextual Models for Object Detection Using Boosted Random Fields 论文

详细信息

发表期刊/会议

DSpace@MIT (Massachusetts Institute of Technology)

发表日期

2004-12-01

发表年份

2004

摘要

We seek to both detect and segment objects in images. To exploit both lo-cal image data as well as contextual information, we introduce Boosted Random Fields (BRFs), which uses Boosting to learn the graph struc-ture and local evidence of a conditional random field (CRF). The graph structure is learned by assembling graph fragments in an additive model. The connections between individual pixels are not very informative, but by using dense graphs, we can pool information from large regions of the image; dense models also support efficient inference. We show how contextual information from other objects can improve detection perfor-mance, both in terms of accuracy and speed, by using a computational cascade. We apply our system to detect stuff and things in office and street scenes. 1