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Learning compressed image classification features

Publication ,  Conference
Qiu, Q; Sapiro, G
Published in: 2014 IEEE International Conference on Image Processing, ICIP 2014
January 28, 2014

Learning a transformation-based dimension reduction, thereby compressive, technique for classification is here proposed. High-dimensional data often approximately lie in a union of low-dimensional subspaces. We propose to perform dimension reduction by learning a 'fat' linear transformation matrix on subspaces using nuclear norm as the optimization criteria. The learned transformation enables dimension reduction, and, at the same time, restores a low-rank structure for data from the same class and maximizes the separation between different classes, thereby improving classification via learned low-dimensional features. Theoretical and experimental results support the proposed framework, which can be interpreted as learning compressing sensing matrices for classification.

Duke Scholars

Published In

2014 IEEE International Conference on Image Processing, ICIP 2014

DOI

Publication Date

January 28, 2014

Start / End Page

5761 / 5765
 

Citation

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Qiu, Q., & Sapiro, G. (2014). Learning compressed image classification features. In 2014 IEEE International Conference on Image Processing, ICIP 2014 (pp. 5761–5765). https://doi.org/10.1109/ICIP.2014.7026165
Qiu, Q., and G. Sapiro. “Learning compressed image classification features.” In 2014 IEEE International Conference on Image Processing, ICIP 2014, 5761–65, 2014. https://doi.org/10.1109/ICIP.2014.7026165.
Qiu Q, Sapiro G. Learning compressed image classification features. In: 2014 IEEE International Conference on Image Processing, ICIP 2014. 2014. p. 5761–5.
Qiu, Q., and G. Sapiro. “Learning compressed image classification features.” 2014 IEEE International Conference on Image Processing, ICIP 2014, 2014, pp. 5761–65. Scopus, doi:10.1109/ICIP.2014.7026165.
Qiu Q, Sapiro G. Learning compressed image classification features. 2014 IEEE International Conference on Image Processing, ICIP 2014. 2014. p. 5761–5765.

Published In

2014 IEEE International Conference on Image Processing, ICIP 2014

DOI

Publication Date

January 28, 2014

Start / End Page

5761 / 5765