Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering

Authors

Xiangguang Dai^{1, *,} , Nian Zhang², Keke Zhang¹, Jiang Xiong¹

¹Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education, Chongqing Three Gorges University, Chongqing 40044, China

²Department of Electrical and Computer Engineering, University of the District of Columbia, Washington, DC 20008, USA

^*Corresponding author. Email: daixiangguang@163.com

DOI

10.2991/ijcis.d.200527.003How to use a DOI?

Keywords

Recovery; Dimensionality reduction; Weighted nonnegative matrix factorization; Noise

Abstract

Conventional nonnegative matrix factorization and its variants cannot separate the noise data space into a clean space and learn an effective low-dimensional subspace from Salt and Pepper noise or Contiguous Occlusion. This paper proposes a weighted nonnegative matrix factorization (WNMF) to improve the robustness of existing nonnegative matrix factorization. In WNMF, a weighted graph is constructed to label the uncorrupted data as 1 and the corrupted data as 0, and an effective matrix factorization model is proposed to recover the noise data and achieve clustering from the recovered data. Extensive experiments on the image datasets corrupted by Salt and Pepper noise or Contiguous Occlusion are presented to demonstrate the effectiveness and robustness of the proposed method in image inpainting and clustering.

Copyright

© 2020 The Authors. Published by Atlantis Press SARL.

Open Access

This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Download article (PDF)
View full text (HTML)

TY  - JOUR
AU  - Xiangguang Dai
AU  - Nian Zhang
AU  - Keke Zhang
AU  - Jiang Xiong
PY  - 2020
DA  - 2020/06/17
TI  - Weighted Nonnegative Matrix Factorization for Image Inpainting and Clustering
JO  - International Journal of Computational Intelligence Systems
SP  - 734
EP  - 743
VL  - 13
IS  - 1
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.d.200527.003
DO  - 10.2991/ijcis.d.200527.003
ID  - Dai2020
ER  -