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Bag of ARSRG Words (BoAW)

M. Manzo,Simone Pellino

Published 2019 in Machine Learning and Knowledge Extraction

ABSTRACT

In recent years researchers have worked to understand image contents in computer vision. In particular, the bag of visual words (BoVW) model, which describes images in terms of a frequency histogram of visual words, is the most adopted paradigm. The main drawback is the lack of information about location and the relationships between features. For this purpose, we propose a new paradigm called bag of ARSRG (attributed relational SIFT (scale-invariant feature transform) regions graph) words (BoAW). A digital image is described as a vector in terms of a frequency histogram of graphs. Adopting a set of steps, the images are mapped into a vector space passing through a graph transformation. BoAW is evaluated in an image classification context on standard datasets and its effectiveness is demonstrated through experimental results compared with well-known competitors.

PUBLICATION RECORD

  • Publication year

    2019

  • Venue

    Machine Learning and Knowledge Extraction

  • Publication date

    2019-08-05

  • Fields of study

    Computer Science

  • Identifiers
    DOI 10.3390/MAKE1030050
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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