Feature mining for image classification
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Feature mining for image classification. / Dollár, Piotr; Tu, Zhuowen; Tao, Hai; Belongie, Serge.
In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2007.Research output: Contribution to journal › Conference article › Research › peer-review
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TY - GEN
T1 - Feature mining for image classification
AU - Dollár, Piotr
AU - Tu, Zhuowen
AU - Tao, Hai
AU - Belongie, Serge
PY - 2007
Y1 - 2007
N2 - The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).
AB - The efficiency and robustness of a vision system is often largely determined by the quality of the image features available to it. In data mining, one typically works with immense volumes of raw data, which demands effective algorithms to explore the data space. In analogy to data mining, the space of meaningful features for image analysis is also quite vast. Recently, the challenges associated with these problem areas have become more tractable through progress made in machine learning and concerted research effort in manual feature design by domain experts. In this paper, we propose a feature mining paradigm for image classification and examine several feature mining strategies. We also derive a principled approach for dealing with features with varying computational demands. Our goal is to alleviate the burden of manual feature design, which is a key problem in computer vision and machine learning. We include an in-depth empirical study on three typical data sets and offer theoretical explanations for the performance of various feature mining strategies. As a final confirmation of our ideas, we show results of a system, that utilizing feature mining strategies matches or outperforms the best reported results on pedestrian classification (where considerable effort has been devoted to expert feature design).
UR - http://www.scopus.com/inward/record.url?scp=34948852777&partnerID=8YFLogxK
U2 - 10.1109/CVPR.2007.383046
DO - 10.1109/CVPR.2007.383046
M3 - Conference article
AN - SCOPUS:34948852777
JO - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
JF - I E E E Conference on Computer Vision and Pattern Recognition. Proceedings
SN - 1063-6919
T2 - 2007 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, CVPR'07
Y2 - 17 June 2007 through 22 June 2007
ER -
ID: 302052341