Max-margin multiattribute learning with low-rank constraint

Qiang Zhang, Lin Chen, Baoxin Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


Attribute learning has attracted a lot of interests in recent years for its advantage of being able to model high-level concepts with a compact set of midlevel attributes. Real-world objects often demand multiple attributes for effective modeling. Most existing methods learn attributes independently without explicitly considering their intrinsic relatedness. In this paper, we propose max margin multiattribute learning with low-rank constraint, which learns a set of attributes simultaneously, using only relative ranking of the attributes for the data. By learning all the attributes simultaneously through low-rank constraint, the proposed method is able to capture their intrinsic correlation for improved learning; by requiring only relative ranking, the method avoids restrictive binary labels of attributes that are often assumed by many existing techniques. The proposed method is evaluated on both synthetic data and real visual data including a challenging video data set. Experimental results demonstrate the effectiveness of the proposed method.

Original languageEnglish (US)
Article number6811202
Pages (from-to)2866-2876
Number of pages11
JournalIEEE Transactions on Image Processing
Issue number7
StatePublished - Jul 2014


  • Multi-task learning
  • attribute learning
  • low rank
  • relative attribute
  • surgical skill

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


Dive into the research topics of 'Max-margin multiattribute learning with low-rank constraint'. Together they form a unique fingerprint.

Cite this