Paper Info
Title
Bilinear Factorization Via Recursive Sample Factoring For Low-Rank Hyperspectral Image Recovery
Abstract
Low-rank hyperspectral image recovery (LRHSIR) is a very challenging task in various computer vision applications for its inherent complexity. Hyperspectral image (HSI) contains much more information than a regular image due to significant number of spectra bands and the spectral information can be considered as multiview. In this paper, a method of bilinear factorization via recursive sample factoring (BF-RSF) is proposed. Different from traditional low rank models with each data point being treated equally, the importance of each data point is measured by the sample factoring that imposes a penalty on each sample in our BF-RSF model. The sample factoring is a cosine similarity metric learnt from the angle between each data point and the principal component of the low-rank matrix in the feature space. That is, the closer a data point to the principal component vector, the more likely it is a clean data point. By imposing the sample factoring onto the training dataset, the outliers or noise will be detected and their effect will be suppressed. Therefore, a better low-rank structure of clean data can be obtained especially in a heavy noisy scenario, with the effect of noisy data points in modeling being suppressed. Extensive experimental results on SalinasA, demonstrate that BF-RSF outperforms state-of-the-art low-rank matrix recovery methods in image clustering tasks with various levels of corruptions.
Year
DOI
Venue
2019
10.1007/978-3-030-34113-8_44
IMAGE AND GRAPHICS, ICIG 2019, PT III
Keywords
DocType
Volume
Hyperspectral image (HSI), Bilinear factorization, Sample factoring, Cosine similarity metric
Conference
11903
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
5
Name
Order
Citations
PageRank
Yuxuan Wang100.34
Timothy Apasiba Abeo201.69
Liangjun Wang374.21
Dickson Keddy Wornyo422.05
Xiangjun Shen55013.58