Bag of spatio-visual words for context inference in scene classification

  • Authors:

  • A. Bolovinou;I. Pratikakis;S. Perantonis

  • Affiliations:

  • Department of Informatics and Telecommunications, University of Athens, Greece and Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Center for Scien ...;Democritus University of Thrace, Department of Electrical and Computer Engineering, GR-67100 Xanthi, Greece;Computational Intelligence Laboratory, Institute of Informatics and Telecommunications, National Center for Scientific Research "Demokritos", 153 10 Athens, Greece

  • Venue:
  • Pattern Recognition
  • Year:
  • 2013

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Abstract

In the ''bag of visual words (BoVW)'' representation each image is represented by an unordered set of visual words. In this paper, a novel approach to encode ordered spatial configurations of visual words in order to add context in the representation is presented. The proposed method introduces a bag of spatio-visual words representation (BoSVW) obtained by clustering of visual words' correlogram ensembles. Specifically, the spherical K-means clustering algorithm is employed accounting for the large dimensionality and the sparsity of the proposed spatio-visual descriptors. Experimental results on four standard datasets show that the proposed method significantly improves a state-of-the-art BoVW model and compares favorably to existing context-based scene classification approaches.