Vegetation Segmentation in Cornfield Images Using Bag of Words

  • Yerania Campos
  • Erik Rodner
  • Joachim Denzler
  • Humberto Sossa
  • Gonzalo Pajares
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10016)


We provide an alternative methodology for vegetation segmentation in cornfield images. The process includes two main steps, which makes the main contribution of this approach: (a) a low-level segmentation and (b) a class label assignment using Bag of Words (BoW) representation in conjunction with a supervised learning framework. The experimental results show our proposal is adequate to extract green plants in images of maize fields. The accuracy for classification is 95.3 % which is comparable to values in current literature.


Bag-of-Words Machine learning Colour Vegetation Indices Green detection 



H. Sossa thanks CONACyT under call: Frontiers of Science (grant number 65) for the economic support. We would like to express our sincere gratitude to Jena University research team for their fruitful comments and suggestions for significant improvement of this work, especially to Sven Sickert who help providing results with ICF algorithm.


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  • Yerania Campos
    • 1
  • Erik Rodner
    • 2
  • Joachim Denzler
    • 2
  • Humberto Sossa
    • 3
  • Gonzalo Pajares
    • 1
  1. 1.Department of Software Engineering and Artificial Intelligence, Faculty of InformaticsComplutense UniversityMadridSpain
  2. 2.Computer Vision GroupFriedrich Schiller University JenaJenaGermany
  3. 3.Instituto Politécnico Nacional-CICMexico D.F.Mexico

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