Name
Title | ||
|---|---|---|
Bilinear low-rank coding framework and extension for robust image recovery and feature representation |
Abstract | ||
|---|---|---|
We mainly explore the low-rank image recovery problem.A bilinear low-rank image coding framework is proposed.For recovery, TLRR preserves both column and row information of given data.Out-of-sample extension of TLRR is presented for handling outside data.We propose two local and global low-rank subspace learning methods for feature learning. We mainly study the low-rank image recovery problem by proposing a bilinear low-rank coding framework called Tensor Low-Rank Representation. For enhanced low-rank recovery and error correction, our method constructs a low-rank tensor subspace to reconstruct given images along row and column directions simultaneously by computing two low-rank matrices alternately from a nuclear norm minimization problem, so both column and row information of data can be effectively preserved. Our bilinear approach seamlessly integrates the low-rank coding and dictionary learning into a unified framework. Thus, our formulation can be treated as enhanced Inductive Robust Principal Component Analysis with noise removed by low-rank representation, and can also be considered as the enhanced low-rank representation with a clean informative dictionary via low-rank embedding. To enable our method to include outside images, the out-of-sample extension is also presented by regularizing the model to correlate image features with the low-rank recovery of the images. Comparison with other criteria shows that our model exhibits stronger robustness and enhanced performance. We also use the outputted bilinear low-rank codes for feature learning. Two unsupervised local and global low-rank subspace learning methods are proposed for extracting image features for classification. Simulations verified the validity of our techniques for image recovery, representation and classification. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1016/j.knosys.2015.06.001 | Knowledge-Based Systems |
Keywords | Field | DocType |
Image recovery,Bilinear low-rank coding,Image representation,Subspace learning,Out-of-sample extension | Embedding,Subspace topology,Pattern recognition,Feature (computer vision),Computer science,Robustness (computer science),Error detection and correction,Robust principal component analysis,Artificial intelligence,Machine learning,Feature learning,Bilinear interpolation | Journal |
Volume | Issue | ISSN |
86 | C | 0950-7051 |
Citations | PageRank | References |
10 | 0.45 | 43 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhao Zhang | 1 | 938 | 65.99 |
| Shuicheng Yan | 2 | 767 | 25.71 |
| Mingbo Zhao | 3 | 631 | 36.16 |
| Fan-Zhang Li | 4 | 109 | 5.63 |