İşcen, AhmetGölge, ErenArmağan, AnılDuygulu, Pınar2016-02-082016-02-082013http://hdl.handle.net/11693/28000Date of Conference: 24-26 April 2013We 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.TurkishComputer visionPart based modelsRandom forestsScene recognitionColor distributionComputational costsPart-based modelsRandom forest classifierRandom forestsScene classificationScene recognitionState-of-art methodsColorComputer visionSignal processingDecision treesConference Paper10.1109/SIU.2013.6531220