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Kronecker Decomposition for Image Classification

  • Sabrina Fontanella
  • Antonio J. Rodríguez-Sánchez
  • Justus Piater
  • Sandor Szedmak
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9822)

Abstract

We propose an image decomposition technique that captures the structure of a scene. An image is decomposed into a matrix that represents the adjacency between the elements of the image and their distance. Images decomposed this way are then classified using a maximum margin regression (MMR) approach where the normal vector of the separating hyperplane maps the input feature vectors into the outputs vectors. Multiclass and multilabel classification are native to MMR, unlike other more classical maximum margin approaches, like SVM. We have tested our approach with the ImageCLEF 2015 multi-label classification task, Pascal VOC and Flickr dataset.

Keywords

ImageCLEF Kronecker decomposition Maximum margin MMR SVM Multi-label classification Medical images 

Notes

Acknowledgement

The research leading to these results has received funding from the EU seventh Framework Programme FP7/2007-2013 under grant agreement no. 270273, Xperience.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Sabrina Fontanella
    • 1
    • 2
  • Antonio J. Rodríguez-Sánchez
    • 1
  • Justus Piater
    • 1
  • Sandor Szedmak
    • 3
  1. 1.Intelligent and Interactive Systems, Department of Computer ScienceUniversity of InnsbruckInnsbruckAustria
  2. 2.Department of Computer ScienceUniversity of SalernoFiscianoItaly
  3. 3.Department of Computer ScienceAalto UniversityEspooFinland

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