Part and appearance sharing: Recursive compositional models for multi-view multi-object detection
Name
Freeman-Part and Appearance.pdf
Size
1.61 MB
Format
Adobe PDF
Checksum (MD5)
4f2854bf3057f8454d28b7c8b6c34e6a
Author(s)
Zhu, Long
Chen, Yuanhao
Torralba, Antonio
Freeman, William T.
Yuille, Alan
Date Issued
August 2010
Journal
2010 IEEE Conference on Computer Vision and Pattern Recognition
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Detection, Multi- et al. “Part and Appearance Sharing: Recursive Compositional Models for Multi-view.” IEEE, 2010. 1919–1926. © Copyright 2010 IEEE
Version
Final published version
Abstract
We propose Recursive Compositional Models (RCMs) for simultaneous multi-view multi-object detection and parsing (e.g. view estimation and determining the positions of the object subparts). We represent the set of objects by a family of RCMs where each RCM is a probability distribution defined over a hierarchical graph which corresponds to a specific object and viewpoint. An RCM is constructed from a hierarchy of subparts/subgraphs which are learnt from training data. Part-sharing is used so that different RCMs are encouraged to share subparts/subgraphs which yields a compact representation for the set of objects and which enables efficient inference and learning from a limited number of training samples. In addition, we use appearance-sharing so that RCMs for the same object, but different viewpoints, share similar appearance cues which also helps efficient learning. RCMs lead to a multi-view multi-object detection system. We illustrate RCMs on four public datasets and achieve state-of-the-art performance.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/CVPR.2010.5539865
