Details
| Original language | English |
|---|---|
| Title of host publication | 2015 International Conference on Computer Vision |
| Subtitle of host publication | ICCV 2015 |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 1035-1043 |
| Number of pages | 9 |
| ISBN (electronic) | 9781467383912 |
| Publication status | Published - Feb 2015 |
| Event | 15th IEEE International Conference on Computer Vision, ICCV 2015 - Santiago, Chile Duration: 11 Dec 2015 → 18 Dec 2015 |
Publication series
| Name | Proceedings of the IEEE International Conference on Computer Vision |
|---|---|
| Volume | 2015 International Conference on Computer Vision, ICCV 2015 |
| ISSN (Print) | 1550-5499 |
Abstract
In this paper, we treat the problem of continuous pose estimation for object categories as a regression problem on the basis of only 2D training information. While regression is a natural framework for continuous problems, regression methods so far achieved inferior results with respect to 3D-based and 2D-based classification-and-refinement approaches. This may be attributed to their weakness to high intra-class variability as well as to noisy matching procedures and lack of geometrical constraints. We propose to apply regression to Fisher-encoded vectors computed from large cells by learning an array of Fisher regressors. Fisher encoding makes our algorithm flexible to variations in class appearance, while the array structure permits to indirectly introduce spatial context information in the approach. We formulate our problem as a MAP inference problem, where the likelihood function is composed of a generative term based on the prediction error generated by the ensemble of Fisher regressors as well as a discriminative term based on SVM classifiers. We test our algorithm on three publicly available datasets that envisage several difficulties, such as high intra-class variability, truncations, occlusions, and motion blur, obtaining state-of-the-art results.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Computer Science(all)
- Computer Vision and Pattern Recognition
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2015 International Conference on Computer Vision: ICCV 2015. Institute of Electrical and Electronics Engineers Inc., 2015. p. 1035-1043 7410481 (Proceedings of the IEEE International Conference on Computer Vision; Vol. 2015 International Conference on Computer Vision, ICCV 2015).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - Continuous Pose Estimation with a Spatial Ensemble of Fisher Regressors
AU - Fenzi, Michele
AU - Leal-Taixe, Laura
AU - Ostermann, Jörn
AU - Tuytelaars, Tinne
PY - 2015/2
Y1 - 2015/2
N2 - In this paper, we treat the problem of continuous pose estimation for object categories as a regression problem on the basis of only 2D training information. While regression is a natural framework for continuous problems, regression methods so far achieved inferior results with respect to 3D-based and 2D-based classification-and-refinement approaches. This may be attributed to their weakness to high intra-class variability as well as to noisy matching procedures and lack of geometrical constraints. We propose to apply regression to Fisher-encoded vectors computed from large cells by learning an array of Fisher regressors. Fisher encoding makes our algorithm flexible to variations in class appearance, while the array structure permits to indirectly introduce spatial context information in the approach. We formulate our problem as a MAP inference problem, where the likelihood function is composed of a generative term based on the prediction error generated by the ensemble of Fisher regressors as well as a discriminative term based on SVM classifiers. We test our algorithm on three publicly available datasets that envisage several difficulties, such as high intra-class variability, truncations, occlusions, and motion blur, obtaining state-of-the-art results.
AB - In this paper, we treat the problem of continuous pose estimation for object categories as a regression problem on the basis of only 2D training information. While regression is a natural framework for continuous problems, regression methods so far achieved inferior results with respect to 3D-based and 2D-based classification-and-refinement approaches. This may be attributed to their weakness to high intra-class variability as well as to noisy matching procedures and lack of geometrical constraints. We propose to apply regression to Fisher-encoded vectors computed from large cells by learning an array of Fisher regressors. Fisher encoding makes our algorithm flexible to variations in class appearance, while the array structure permits to indirectly introduce spatial context information in the approach. We formulate our problem as a MAP inference problem, where the likelihood function is composed of a generative term based on the prediction error generated by the ensemble of Fisher regressors as well as a discriminative term based on SVM classifiers. We test our algorithm on three publicly available datasets that envisage several difficulties, such as high intra-class variability, truncations, occlusions, and motion blur, obtaining state-of-the-art results.
UR - http://www.scopus.com/inward/record.url?scp=84973910917&partnerID=8YFLogxK
U2 - 10.1109/ICCV.2015.124
DO - 10.1109/ICCV.2015.124
M3 - Conference contribution
AN - SCOPUS:84973910917
T3 - Proceedings of the IEEE International Conference on Computer Vision
SP - 1035
EP - 1043
BT - 2015 International Conference on Computer Vision
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 15th IEEE International Conference on Computer Vision, ICCV 2015
Y2 - 11 December 2015 through 18 December 2015
ER -