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

@inproceedings{Frhlich2012SemanticSW,
  title={Semantic Segmentation with Millions of Features: Integrating Multiple Cues in a Combined Random Forest Approach},
  author={Bj{\"o}rn Fr{\"o}hlich and Erik Rodner and Joachim Denzler},
  booktitle={ACCV},
  year={2012}
}
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… CONTINUE READING
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