Multi-component Models for Object Detection
Venue
European Conference on Computer Vision, Springer (2012), Volume 4, 445-458
Publication Year
2012
Authors
Chunhui Gu, Pablo Arbelaez, Yuanqing Lin, Kai Yu, Jitendra Malik
BibTeX
Abstract
In this paper, we propose a multi-component approach for object detection. Rather
than attempting to represent an object category with a monolithic model, or
pre-defining a reduced set of aspects, we form visual clusters from the data that
are tight in appearance and configuration spaces. We train individual classifiers
for each component, and then learn a second classifier that operates at the
category level by aggregating responses from multiple components. In order to
reduce computation cost during detection, we adopt the idea of object window
selection, and our segmentation-based selection mechanism produces fewer than 500
windows per image while preserving high object recall. When compared to the leading
methods in the challenging VOC PASCAL 2010 dataset, our multi-component approach
obtains highly competitive results. Furthermore, unlike monolithic detection
methods, our approach allows the transfer of finer-grained semantic information
from the components, such as keypoint location and segmentation masks.
