Reconstruction-free inference on compressive measurements

Suhas Lohit, Kuldeep Kulkarni, Pavan Turaga, Jian Wang, Aswin C. Sankaranarayanan

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

16 Scopus citations

Abstract

Spatial-multiplexing cameras have emerged as a promising alternative to classical imaging devices, often enabling acquisition of 'more for less'. One popular architecture for spatial multiplexing is the single-pixel camera (SPC), which acquires coded measurements of the scene with pseudo-random spatial masks. Significant theoretical developments over the past few years provide a means for reconstruction of the original imagery from coded measurements at sub-Nyquist sampling rates. Yet, accurate reconstruction generally requires high measurement rates and high signal-to-noise ratios. In this paper, we enquire if one can perform high-level visual inference problems (e.g. face recognition or action recognition) from compressive cameras without the need for image reconstruction. This is an interesting question since in many practical scenarios, our goals extend beyond image reconstruction. However, most inference tasks often require non-linear features and it is not clear how to extract such features directly from compressed measurements. In this paper, we show that one can extract nontrivial correlational features directly without reconstruction of the imagery. As a specific example, we consider the problem of face recognition beyond the visible spectrum e.g in the short-wave infra-red region (SWIR)-where pixels are expensive. We base our framework on smashed filters which suggests that inner-products between high-dimensional signals can be computed in the compressive domain to a high degree of accuracy. We collect a new face image dataset of 30 subjects, obtained using an SPC. Using face recognition as an example, we show that one can indeed perform reconstruction-free inference with a very small loss of accuracy at very high compression ratios of 100 and more.

Original languageEnglish (US)
Title of host publication2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015
PublisherIEEE Computer Society
Pages16-24
Number of pages9
ISBN (Electronic)9781467367592
DOIs
StatePublished - Oct 19 2015
EventIEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 - Boston, United States
Duration: Jun 7 2015Jun 12 2015

Publication series

NameIEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Volume2015-October
ISSN (Print)2160-7508
ISSN (Electronic)2160-7516

Other

OtherIEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015
CountryUnited States
CityBoston
Period6/7/156/12/15

Keywords

  • Correlation
  • Databases
  • Face
  • Face recognition
  • Feature extraction
  • Image coding
  • Image reconstruction

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition
  • Electrical and Electronic Engineering

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    Lohit, S., Kulkarni, K., Turaga, P., Wang, J., & Sankaranarayanan, A. C. (2015). Reconstruction-free inference on compressive measurements. In 2015 IEEE Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2015 (pp. 16-24). [7301371] (IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops; Vol. 2015-October). IEEE Computer Society. https://doi.org/10.1109/CVPRW.2015.7301371