Opportunistic sensing for object recognition - A unified formulation for dynamic sensor selection and feature extraction

Zhaowen Wang, Jianchao Yang, Nasser Nasrabadi, Jiangping Wang, Thomas Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A novel problem of object recognition with dynamically allocated sensing resources is considered in this paper. We call this problem opportunistic sensing since prior knowledge about the correlation between class label and signal distribution is exploited as early as in data acquisition. Two forms of sensing parameters - discrete sensor index and continuous linear measurement vector - are optimized within the same maximum negative entropy framework. The computationally intractable expected entropy is approximated using unscented transform for Gaussian models, and we solve the problem using a gradient-based method. Our formulation is theoretically shown to be closely related to the maximum mutual information criterion for sensor selection and linear feature extraction techniques such as PCA, LDA, and CCA. The proposed approach is validated on multi-view vehicle classification and face recognition datasets, and remarkable improvement over baseline methods is demonstrated in the experiments.

Original languageEnglish (US)
Title of host publication2013 IEEE International Conference on Multimedia and Expo, ICME 2013
DOIs
StatePublished - Oct 21 2013
Event2013 IEEE International Conference on Multimedia and Expo, ICME 2013 - San Jose, CA, United States
Duration: Jul 15 2013Jul 19 2013

Publication series

NameProceedings - IEEE International Conference on Multimedia and Expo
ISSN (Print)1945-7871
ISSN (Electronic)1945-788X

Other

Other2013 IEEE International Conference on Multimedia and Expo, ICME 2013
CountryUnited States
CitySan Jose, CA
Period7/15/137/19/13

Keywords

  • feature extraction
  • objection recognition
  • opportunistic sensing
  • view selection

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications

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