A non-negative tensor factorization approach to feature extraction for image analysis
A non-negative tensor factorization approach to feature extraction for image analysis
In this paper, a decomposition method is proposed for Separable Non-negative Tensor Factorization (SNTF), which yields a structure similar to the PARATUCK2 model for the decomposition of non-negative tensors. Among many different possibilities for performing tensor factorization, we develop a specific procedure for SNTF with an aim to decompose multi-way dataset expressed in the form of a tensor into low-rank components that extract dominant features in the data. The SNTF method is evaluated using real image data and the results show that the proposed SNTF is superior to other NTF methods in terms of error performance and computational efficiency.
Image Segmentation, Non-negative Matrix Factorization, Non-negative Tensor Factorization, Tensor Decomposition
168-171
Man Shun Ang, Andersen
ed509ecd-39a3-4887-a709-339fdaded867
Sam Hung, Yeung
f265b6e6-2e99-4c6d-b674-d9140994d137
Zhang, Zhiguo
355cd272-89ec-4566-9b11-a75fffa16ed0
2 July 2016
Man Shun Ang, Andersen
ed509ecd-39a3-4887-a709-339fdaded867
Sam Hung, Yeung
f265b6e6-2e99-4c6d-b674-d9140994d137
Zhang, Zhiguo
355cd272-89ec-4566-9b11-a75fffa16ed0
Man Shun Ang, Andersen, Sam Hung, Yeung and Zhang, Zhiguo
(2016)
A non-negative tensor factorization approach to feature extraction for image analysis.
In Proceedings - 2016 IEEE International Conference on Digital Signal Processing, DSP 2016.
vol. 0,
IEEE.
.
(doi:10.1109/ICDSP.2016.7868538).
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Conference or Workshop Item
(Paper)
Abstract
In this paper, a decomposition method is proposed for Separable Non-negative Tensor Factorization (SNTF), which yields a structure similar to the PARATUCK2 model for the decomposition of non-negative tensors. Among many different possibilities for performing tensor factorization, we develop a specific procedure for SNTF with an aim to decompose multi-way dataset expressed in the form of a tensor into low-rank components that extract dominant features in the data. The SNTF method is evaluated using real image data and the results show that the proposed SNTF is superior to other NTF methods in terms of error performance and computational efficiency.
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Published date: 2 July 2016
Additional Information:
Publisher Copyright:
© 2016 IEEE.
Venue - Dates:
2016 IEEE International Conference on Digital Signal Processing, DSP 2016, , Beijing, China, 2016-10-16 - 2016-10-18
Keywords:
Image Segmentation, Non-negative Matrix Factorization, Non-negative Tensor Factorization, Tensor Decomposition
Identifiers
Local EPrints ID: 495246
URI: http://eprints.soton.ac.uk/id/eprint/495246
PURE UUID: 9105e943-005f-4e1a-9b36-cd2d433ecca9
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Date deposited: 04 Nov 2024 17:33
Last modified: 05 Nov 2024 03:05
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Contributors
Author:
Andersen Man Shun Ang
Author:
Yeung Sam Hung
Author:
Zhiguo Zhang
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