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A Novel Graph Embedding Framework for Object Recognition

  • Mario Manzo
  • Simone Pellino
  • Alfredo PetrosinoEmail author
  • Alessandro Rozza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8928)

Abstract

A great deal of research works have been devoted to understand image contents. In this field many well-known methods exploit Bag of Words (BoW) features describing image contents as appearance frequency histogram of visual words. These approaches have a main drawback, the location information and the relationships between features are lost. To overcame this limitation we propose a novel methodology for the Object recognition task. A digital image is described as a feature vector computed by means of a new graph embedding paradigm on the Attributed Relational SIFT Regions Graph. The final classification is performed by using Logistic Label Propagation classifier. Our framework is evaluated on standard databases (such as ETH-\(80\), COIL-\(100\) and ALOI) and the achieved results compared with those obtained by well-known methodologies confirm its quality.

Keywords

Image classification Object recognition Graph based image representation Graph embedding 

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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Mario Manzo
    • 1
  • Simone Pellino
    • 1
  • Alfredo Petrosino
    • 1
    Email author
  • Alessandro Rozza
    • 2
  1. 1.University of Naples ParthenopeNaplesItaly
  2. 2.Research Team - Hyera SoftwareCoccaglioItaly

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