IEICE TRANSACTIONS on Information
Deformable Part-Based Model Transfer for Object Detection
Summary :
The transfer of prior knowledge from source domains can improve the performance of learning when the training data in a target domain are insufficient. In this paper we propose a new strategy to transfer deformable part models (DPMs) for object detection, using offline-trained auxiliary DPMs of similar categories as source models to improve the performance of the target object detector. A DPM presents an object by using a root filter and several part filters. We use these filters of the auxiliary DPMs as prior knowledge and adapt the filters to the target object. With a latent transfer learning method, appropriate local features are extracted for the transfer of part filters. Our experiments demonstrate that this strategy can lead to a detector superior to some state-of-the-art methods.
- Publication
- IEICE TRANSACTIONS on Information Vol.E97-D No.5 pp.1394-1397
- Publication Date
- 2014/05/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E97.D.1394
- Type of Manuscript
- LETTER
- Category
- Image Recognition, Computer Vision
Authors
Zhiwei RUAN
Tsinghua University
Guijin WANG
Tsinghua University
Xinggang LIN
Tsinghua University
Jing-Hao XUE
University College London
Yong JIANG
Canon Information Technology (Beijing) Co., LTD
Keyword
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Zhiwei RUAN, Guijin WANG, Xinggang LIN, Jing-Hao XUE, Yong JIANG, "Deformable Part-Based Model Transfer for Object Detection" in IEICE TRANSACTIONS on Information,
vol. E97-D, no. 5, pp. 1394-1397, May 2014, doi: 10.1587/transinf.E97.D.1394.
Abstract: The transfer of prior knowledge from source domains can improve the performance of learning when the training data in a target domain are insufficient. In this paper we propose a new strategy to transfer deformable part models (DPMs) for object detection, using offline-trained auxiliary DPMs of similar categories as source models to improve the performance of the target object detector. A DPM presents an object by using a root filter and several part filters. We use these filters of the auxiliary DPMs as prior knowledge and adapt the filters to the target object. With a latent transfer learning method, appropriate local features are extracted for the transfer of part filters. Our experiments demonstrate that this strategy can lead to a detector superior to some state-of-the-art methods.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E97.D.1394/_p
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@ARTICLE{e97-d_5_1394,
author={Zhiwei RUAN, Guijin WANG, Xinggang LIN, Jing-Hao XUE, Yong JIANG, },
journal={IEICE TRANSACTIONS on Information},
title={Deformable Part-Based Model Transfer for Object Detection},
year={2014},
volume={E97-D},
number={5},
pages={1394-1397},
abstract={The transfer of prior knowledge from source domains can improve the performance of learning when the training data in a target domain are insufficient. In this paper we propose a new strategy to transfer deformable part models (DPMs) for object detection, using offline-trained auxiliary DPMs of similar categories as source models to improve the performance of the target object detector. A DPM presents an object by using a root filter and several part filters. We use these filters of the auxiliary DPMs as prior knowledge and adapt the filters to the target object. With a latent transfer learning method, appropriate local features are extracted for the transfer of part filters. Our experiments demonstrate that this strategy can lead to a detector superior to some state-of-the-art methods.},
keywords={},
doi={10.1587/transinf.E97.D.1394},
ISSN={1745-1361},
month={May},}
Copy
TY - JOUR
TI - Deformable Part-Based Model Transfer for Object Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1394
EP - 1397
AU - Zhiwei RUAN
AU - Guijin WANG
AU - Xinggang LIN
AU - Jing-Hao XUE
AU - Yong JIANG
PY - 2014
DO - 10.1587/transinf.E97.D.1394
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E97-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2014
AB - The transfer of prior knowledge from source domains can improve the performance of learning when the training data in a target domain are insufficient. In this paper we propose a new strategy to transfer deformable part models (DPMs) for object detection, using offline-trained auxiliary DPMs of similar categories as source models to improve the performance of the target object detector. A DPM presents an object by using a root filter and several part filters. We use these filters of the auxiliary DPMs as prior knowledge and adapt the filters to the target object. With a latent transfer learning method, appropriate local features are extracted for the transfer of part filters. Our experiments demonstrate that this strategy can lead to a detector superior to some state-of-the-art methods.
ER -
English
