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This paper proposes a semantic segmentation method for outdoor scenes captured by a surveillance camera. Our algorithm classifies each perceptually homogenous region as one of the predefined classes learned from a collection of manually labelled images. The proposed approach combines two different types of information. First, color segmentation is performed to divide the scene into perceptually similar regions. Then, the second step is based on SIFT keypoints and uses the bag of wordsdoi:10.1109/isivc.2016.7893967 dblp:conf/isivc/BouachirTBB16 fatcat:bg4f2kg6hfa6renpg7e62oilo4