Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification

Xiaofeng Zhu, Qing Xie, Yonghua Zhu, Xingyi Liu, Shichao Zhang

Research output: Contribution to journalArticlepeer-review

13 Scopus citations


This paper addresses the problem of multi-class image classification by proposing a novel multi-view multi-sparsity kernel reconstruction (MMKR for short) model. Given images (including test images and training images) representing with multiple visual features, the MMKR first maps them into a high-dimensional space, e.g., a reproducing kernel Hilbert space (RKHS), where test images are then linearly reconstructed by some representative training images, rather than all of them. Furthermore a classification rule is proposed to classify test images. Experimental results on real datasets show the effectiveness of the proposed MMKR while comparing to state-of-the-art algorithms.
Original languageEnglish (US)
Pages (from-to)43-49
Number of pages7
StatePublished - May 29 2015

ASJC Scopus subject areas

  • Artificial Intelligence
  • Cognitive Neuroscience
  • Computer Science Applications


Dive into the research topics of 'Multi-view Multi-sparsity Kernel Reconstruction for Multi-class Image Classification'. Together they form a unique fingerprint.

Cite this