IEICE TRANSACTIONS on Information
Discriminative Middle-Level Parts Mining for Object Detection
Summary :
Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.
- Publication
- IEICE TRANSACTIONS on Information Vol.E98-D No.11 pp.1950-1957
- Publication Date
- 2015/11/01
- Publicized
- 2015/08/03
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2015EDP7200
- Type of Manuscript
- PAPER
- Category
- Image Recognition, Computer Vision
Authors
Dong LI
Tsinghua University
Yali LI
Tsinghua University
Shengjin WANG
Tsinghua University
Keyword
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Dong LI, Yali LI, Shengjin WANG, "Discriminative Middle-Level Parts Mining for Object Detection" in IEICE TRANSACTIONS on Information,
vol. E98-D, no. 11, pp. 1950-1957, November 2015, doi: 10.1587/transinf.2015EDP7200.
Abstract: Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7200/_p
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@ARTICLE{e98-d_11_1950,
author={Dong LI, Yali LI, Shengjin WANG, },
journal={IEICE TRANSACTIONS on Information},
title={Discriminative Middle-Level Parts Mining for Object Detection},
year={2015},
volume={E98-D},
number={11},
pages={1950-1957},
abstract={Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.},
keywords={},
doi={10.1587/transinf.2015EDP7200},
ISSN={1745-1361},
month={November},}
Copy
TY - JOUR
TI - Discriminative Middle-Level Parts Mining for Object Detection
T2 - IEICE TRANSACTIONS on Information
SP - 1950
EP - 1957
AU - Dong LI
AU - Yali LI
AU - Shengjin WANG
PY - 2015
DO - 10.1587/transinf.2015EDP7200
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E98-D
IS - 11
JA - IEICE TRANSACTIONS on Information
Y1 - November 2015
AB - Middle-level parts have attracted great attention in the computer vision community, acting as discriminative elements for objects. In this paper we propose an unsupervised approach to mine discriminative parts for object detection. This work features three aspects. First, we introduce an unsupervised, exemplar-based training process for part detection. We generate initial parts by selective search and then train part detectors by exemplar SVM. Second, a part selection model based on consistency and distinctiveness is constructed to select effective parts from the candidate pool. Third, we combine discriminative part mining with the deformable part model (DPM) for object detection. The proposed method is evaluated on the PASCAL VOC2007 and VOC2010 datasets. The experimental results demons-trate the effectiveness of our method for object detection.
ER -
English
