Sparse label-indicator optimization methods for image classification

Liping Jing, Kwok Po NG

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)

Abstract

Image label prediction is a critical issue in computer vision and machine learning. In this paper, we propose and develop sparse label-indicator optimization methods for image classification problems. Sparsity is introduced in the label-indicator such that relevant and irrelevant images with respect to a given class can be distinguished. Also, when we deal with multi-class image classification problems, the number of possible classes of a given image can also be constrained to be small in which it is valid for natural images. The resulting sparsity model can be formulated as a convex optimization problem, and it can be solved very efficiently. Experimental results are reported to illustrate the effectiveness of the proposed model, and demonstrate that the classification performance of the proposed method is better than the other testing methods in this paper.

Original languageEnglish
Article number6680740
Pages (from-to)1002-1014
Number of pages13
JournalIEEE Transactions on Image Processing
Volume23
Issue number3
DOIs
Publication statusPublished - Mar 2014

Scopus Subject Areas

  • Software
  • Computer Graphics and Computer-Aided Design

User-Defined Keywords

  • Graph
  • image classification
  • multi-class
  • random walk with restart
  • semi-supervised learning
  • sparsity

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