Spatial locality-aware sparse coding and dictionary learning

Jiang Wang, Junsong Yuan, Zhuoyuan Chen, Ying Wu

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations


Nonlinear encoding of SIFT features has recently shown good promise in image classification. This scheme is able to reduce the training complexity of the traditional bag-of-feature approaches while achieving better performance. As a result, it is suitable for large-scale image classification applications. However, existing nonlinear encoding methods do not explicitly consider the spatial relationship when encoding the local features, but merely leaving the spatial information used at a later stage, e.g. through the spatial pyramid matching, is largely inadequate. In this paper, we propose a joint sparse coding and dictionary learning scheme that take the spatial information into consideration in encoding. Our experiments on synthetic data and benchmark data demonstrate that the proposed scheme can learn a better dictionary and achieve higher classification accuracy.

Original languageEnglish (US)
Pages (from-to)491-505
Number of pages15
JournalJournal of Machine Learning Research
StatePublished - Dec 1 2012
Event4th Asian Conference on Machine Learning, ACML 2012 - Singapore, Singapore
Duration: Nov 4 2012Nov 6 2012

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Statistics and Probability
  • Artificial Intelligence

Fingerprint Dive into the research topics of 'Spatial locality-aware sparse coding and dictionary learning'. Together they form a unique fingerprint.

Cite this