U-PC: Unsupervised Planogram Compliance View Full Text


Ontology type: schema:Chapter     


Chapter Info

DATE

2018-10-06

AUTHORS

Archan Ray , Nishant Kumar , Avishek Shaw , Dipti Prasad Mukherjee

ABSTRACT

We present an end-to-end solution for recognizing merchandise displayed in the shelves of a supermarket. Given images of individual products, which are taken under ideal illumination for product marketing, the challenge is to find these products automatically in the images of the shelves. Note that the images of shelves are taken using hand-held camera under store level illumination. We provide a two-layer hypotheses generation and verification model. In the first layer, the model predicts a set of candidate merchandise at a specific location of the shelf while in the second layer, the hypothesis is verified by a novel graph theoretic approach. The performance of the proposed approach on two publicly available datasets is better than the competing approaches by at least 10%. More... »

PAGES

598-613

References to SciGraph publications

  • 2014. Deep Features for Text Spotting in COMPUTER VISION – ECCV 2014
  • 2004-10. Scale & Affine Invariant Interest Point Detectors in INTERNATIONAL JOURNAL OF COMPUTER VISION
  • 2007. Where’s the Weet-Bix? in COMPUTER VISION – ACCV 2007
  • 2006. SURF: Speeded Up Robust Features in COMPUTER VISION – ECCV 2006
  • 2014. Recognizing Products: A Per-exemplar Multi-label Image Classification Approach in COMPUTER VISION – ECCV 2014
  • 2012. Unsupervised Discovery of Mid-Level Discriminative Patches in COMPUTER VISION – ECCV 2012
  • Book

    TITLE

    Computer Vision – ECCV 2018

    ISBN

    978-3-030-01248-9
    978-3-030-01249-6

    Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1007/978-3-030-01249-6_36

    DOI

    http://dx.doi.org/10.1007/978-3-030-01249-6_36

    DIMENSIONS

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