Semantic Scene Classification with Generalized Gaussian Mixture Models

  • Tarek Elguebaly
  • Nizar BouguilaEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9164)


The work proposed in this paper is motivated by the need to develop powerful approaches for scene classification which is a challenging problem mainly due to varying conditions. In this paper, we are mostly interested in automatically assigning a scene image to a semantic category when RGB channels and near infrared information are simultaneously available. We represent images by a collection of local image patches that we use to learn a global generalized Gaussian mixture (GGM) using the split and merge expectation-maximization (SMEM) algorithm. Using this approach, we built an effective scene classification system capable of handling outliers and noise level in the data. Extensive experiments show the merits of the proposed framework.



The completion of this research was made possible thanks to the Natural Sciences and Engineering Research Council of Canada (NSERC).


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Concordia Institute for Information Systems EngineeringConcordia UniversityMontrealCanada

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