Feature regression for multimodal image analysis

Michael Ying Yang; Xuanzi Yong; Bodo Rosenhahn

doi:10.1109/CVPRW.2014.118

Feature regression for multimodal image analysis

Michael Ying Yang^*, Xuanzi Yong, Bodo Rosenhahn

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

2 Citations (Scopus)

Abstract

In this paper, we analyze the relationship between the corresponding descriptors computed from multimodal images with focus on visual and infrared images. First the descriptors are regressed by means of linear regression as well as Gaussian process. We apply different covariance functions and inference methods for Gaussian process. Then the descriptors detected from visual images are mapped to infrared images through the regression results. Predictions are assessed in two ways: the statistics of absolute error between true values and actual values, and the precision score of matching the predicted descriptors to the original infrared descriptors. Experimental results show that regression methods achieve a well-assessed relationship between corresponding descriptors from multiple modalities.

Original language	English
Title of host publication	IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014
Place of Publication	Columbus
Publisher	IEEE
Pages	770-777
Number of pages	8
ISBN (Electronic)	9781479943098, 9781479943098
DOIs	https://doi.org/10.1109/CVPRW.2014.118
Publication status	Published - 24 Sept 2014
Externally published	Yes
Event	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014 - Columbus, OH, USA, Columbus, United States Duration: 23 Jun 2014 → 28 Jun 2014 Conference number: 27

Publication series

Name	IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
ISSN (Print)	2160-7508
ISSN (Electronic)	2160-7516

Conference

Conference	27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014
Abbreviated title	CVPR 2014
Country/Territory	United States
City	Columbus
Period	23/06/14 → 28/06/14
Other	23-28 June 2014

Access to Document

10.1109/CVPRW.2014.118

Cite this

@inproceedings{96891b3ecd604592bc263b4894915a46,

title = "Feature regression for multimodal image analysis",

abstract = "In this paper, we analyze the relationship between the corresponding descriptors computed from multimodal images with focus on visual and infrared images. First the descriptors are regressed by means of linear regression as well as Gaussian process. We apply different covariance functions and inference methods for Gaussian process. Then the descriptors detected from visual images are mapped to infrared images through the regression results. Predictions are assessed in two ways: the statistics of absolute error between true values and actual values, and the precision score of matching the predicted descriptors to the original infrared descriptors. Experimental results show that regression methods achieve a well-assessed relationship between corresponding descriptors from multiple modalities.",

author = "Yang, {Michael Ying} and Xuanzi Yong and Bodo Rosenhahn",

year = "2014",

month = sep,

day = "24",

doi = "10.1109/CVPRW.2014.118",

language = "English",

series = "IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops",

publisher = "IEEE",

pages = "770--777",

booktitle = "IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014",

address = "United States",

note = "27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, CVPR 2014 ; Conference date: 23-06-2014 Through 28-06-2014",

}

Yang, MY, Yong, X & Rosenhahn, B 2014, Feature regression for multimodal image analysis. in IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014., 6910069, IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, IEEE, Columbus, pp. 770-777, 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, Ohio, United States, 23/06/14. https://doi.org/10.1109/CVPRW.2014.118

Feature regression for multimodal image analysis. / Yang, Michael Ying; Yong, Xuanzi; Rosenhahn, Bodo.
IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014. Columbus: IEEE, 2014. p. 770-777 6910069 (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review

TY - GEN

T1 - Feature regression for multimodal image analysis

AU - Yang, Michael Ying

AU - Yong, Xuanzi

AU - Rosenhahn, Bodo

N1 - Conference code: 27

PY - 2014/9/24

Y1 - 2014/9/24

N2 - In this paper, we analyze the relationship between the corresponding descriptors computed from multimodal images with focus on visual and infrared images. First the descriptors are regressed by means of linear regression as well as Gaussian process. We apply different covariance functions and inference methods for Gaussian process. Then the descriptors detected from visual images are mapped to infrared images through the regression results. Predictions are assessed in two ways: the statistics of absolute error between true values and actual values, and the precision score of matching the predicted descriptors to the original infrared descriptors. Experimental results show that regression methods achieve a well-assessed relationship between corresponding descriptors from multiple modalities.

AB - In this paper, we analyze the relationship between the corresponding descriptors computed from multimodal images with focus on visual and infrared images. First the descriptors are regressed by means of linear regression as well as Gaussian process. We apply different covariance functions and inference methods for Gaussian process. Then the descriptors detected from visual images are mapped to infrared images through the regression results. Predictions are assessed in two ways: the statistics of absolute error between true values and actual values, and the precision score of matching the predicted descriptors to the original infrared descriptors. Experimental results show that regression methods achieve a well-assessed relationship between corresponding descriptors from multiple modalities.

UR - https://ezproxy2.utwente.nl/login?url=https://doi.org/10.1109/CVPRW.2014.118

UR - https://ezproxy2.utwente.nl/login?url=https://library.itc.utwente.nl/login/2014/chap/yang_fea.pdf

U2 - 10.1109/CVPRW.2014.118

DO - 10.1109/CVPRW.2014.118

M3 - Conference contribution

AN - SCOPUS:84908548323

T3 - IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops

SP - 770

EP - 777

BT - IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2014

PB - IEEE

CY - Columbus

T2 - 27th IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014

Y2 - 23 June 2014 through 28 June 2014

ER -

Feature regression for multimodal image analysis

Abstract

Publication series

Conference

Access to Document

Other files and links

Fingerprint

Cite this