A Bag of Words Approach for Semantic Segmentation of Monitored Scenes
release_rhxgqma4kfdediw374su5huhhm
by
Wassim Bouachir, Atousa Torabi, Guillaume-Alexandre Bilodeau, Pascal
Blais
2013
Abstract
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 words representation of the regions for the classification.
The prediction is done using a Na\"ive Bayesian Network as a generative
classifier. Compared to existing techniques, our method provides more compact
representations of scene contents and the segmentation result is more
consistent with human perception due to the combination of the color
information with the image keypoints. The experiments conducted on a publicly
available data set demonstrate the validity of the proposed method.
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