Exploring canonical correlation analysis with subspace and structured sparsity for web image annotation

Liang Tao*, Horace H.S. Ip, Aijun ZHANG, Xin Shu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

2 Citations (Scopus)

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 languageEnglish
Pages (from-to)22-30
Number of pages9
JournalImage and Vision Computing
Volume54
DOIs
Publication statusPublished - 1 Oct 2016

Scopus Subject Areas

  • Signal Processing
  • Computer Vision and Pattern Recognition

User-Defined Keywords

  • Canonical correlation
  • Image annotation
  • Sparsity
  • Subspace learning

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