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Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach

  • Björn Fröhlich
  • Erik Rodner
  • Joachim Denzler
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7724)

Abstract

In this paper, we present a new combined approach for feature extraction, classification, and context modeling in an iterative framework based on random decision trees and a huge amount of features. A major focus of this paper is to integrate different kinds of feature types like color, geometric context, and auto context features in a joint, flexible and fast manner. Furthermore, we perform an in-depth analysis of multiple feature extraction methods and different feature types. Extensive experiments are performed on challenging facade recognition datasets, where we show that our approach significantly outperforms previous approaches with a performance gain of more than 15% on the most difficult dataset.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Björn Fröhlich
    • 1
  • Erik Rodner
    • 1
  • Joachim Denzler
    • 1
  1. 1.Computer Vision GroupFriedrich Schiller University JenaGermany

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