Computer Vision and Pattern Recognition, IEEE
Computer Society Washington, DC, USA (2012), pp.
Pablo Arbelaez, Bharath Hariharan, Chunhui Gu, Saurabh Gupta, Lubomir Bourdev,
We address the problem of segmenting and recognizing objects in real world images,
focusing on challenging articulated categories such as humans and other animals.
For this purpose, we propose a novel design for region-based object detectors that
integrates efficiently top-down information from scanning-windows part models and
global appearance cues. Our detectors produce class-specific scores for bottom-up
regions, and then aggregate the votes of multiple overlapping candidates through
pixel classification. We evaluate our approach on the PASCAL segmentation
challenge, and report competitive performance with respect to current leading
techniques. On VOC2010, our method obtains the best results in 6/20 categories and
the highest performance on articulated objects.