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      • Department of Computer Engineering
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      Scene classification with random forests and object and color distributions

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      Author
      İşcen, Ahmet
      Gölge, Eren
      Armağan, Anıl
      Duygulu, Pınar
      Date
      2013
      Source Title
      2013 21st Signal Processing and Communications Applications Conference (SIU)
      Publisher
      IEEE
      Language
      Turkish
      Type
      Conference Paper
      Item Usage Stats
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      Abstract
      We propose a method to recognize the scene of an image by finding the objects and the colors it contains. We approach this problem by creating a binary vector of detected objects and a histogram of the colors that the image contains. We then use these features to train a random forest classifier in order to determine the scene of each image. For class-based classifiers, our method gives comparable results with the state of art methods, such as Object Bank method, for the indoor scene dataset that we used. Additionally, while well-known methods are computationally expensive, our method has a low computational cost. © 2013 IEEE.
      Keywords
      Computer vision
      Part based models
      Random forests
      Scene recognition
      Color distribution
      Computational costs
      Part-based models
      Random forest classifier
      Random forests
      Scene classification
      Scene recognition
      State-of-art methods
      Color
      Computer vision
      Signal processing
      Decision trees
      Permalink
      http://hdl.handle.net/11693/28000
      Published Version (Please cite this version)
      http://dx.doi.org/10.1109/SIU.2013.6531220
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      • Department of Computer Engineering 1413
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