Abstract
Canonical correlation analysis (CCA) has been extensively exploited for modelling Internet multimedia. However, two major challenges are raised for the classical CCA. First, CCA frequently fails to remove noisy and irrelevant features. Second, CCA cannot effectively capture the correlation between semantic labels, which is especially beneficial for annotating web images. In this paper, we propose a new framework that integrates structural sparsity and low-rank shared subspace into the least-squares formulation of CCA. Under this framework, multiple label interactions can be uncovered by the shared common structure of the input space. Meanwhile, a few highly discriminative features can be decided via the structural sparse norm. Owing to the presence of non-smooth structured sparsity, a new efficient iterative algorithm is derived with guaranteed convergence. The empirical studies over several popular web image data collections consistently deliver the effectiveness of our new formulation in comparison with competing algorithms.
Original language | English |
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Pages (from-to) | 22-30 |
Number of pages | 9 |
Journal | Image and Vision Computing |
Volume | 54 |
DOIs | |
Publication status | Published - 1 Oct 2016 |
Scopus Subject Areas
- Signal Processing
- Computer Vision and Pattern Recognition
User-Defined Keywords
- Canonical correlation
- Image annotation
- Sparsity
- Subspace learning