Multiple kernel-based dictionary learning for weakly supervised classification

Ashish Shrivastava, Jaishanker K. Pillai, Vishal M. Patel

Research output: Contribution to journalArticlepeer-review

13 Scopus citations

Abstract

Abstract In this paper, we develop a multiple instance learning (MIL) algorithm using the dictionary learning framework where the labels are given in the form of positive and negative bags, with each bag containing multiple samples. A positive bag is guaranteed to have only one positive class sample while all the samples in a negative bag belong to the negative class. Given positive and negative bags of data, our method learns appropriate feature space to select positive samples from the positive bags as well as optimal dictionaries to represent data in these bags. We apply this method for digit recognition, action recognition, and gender recognition tasks and demonstrate that the proposed method is robust and can perform significantly better than many competitive two class MIL classification algorithms.

Original languageEnglish (US)
Article number5373
Pages (from-to)2667-2675
Number of pages9
JournalPattern Recognition
Volume48
Issue number8
DOIs
StatePublished - Aug 1 2015

All Science Journal Classification (ASJC) codes

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Keywords

  • Dictionary learning
  • Multiple instance learning
  • Multiple kernel learning

Fingerprint Dive into the research topics of 'Multiple kernel-based dictionary learning for weakly supervised classification'. Together they form a unique fingerprint.

Cite this