  This paper proposes a vision-based fire and smoke segmentation system which
use spatial, temporal and motion information to extract the desired regions
from the video frames. The fusion of information is done using multiple
features such as optical flow, divergence and intensity values. These features
extracted from the images are used to segment the pixels into different classes
in an unsupervised way. A comparative analysis is done by using multiple
clustering algorithms for segmentation. Here the Markov Random Field performs
more accurately than other segmentation algorithms since it characterizes the
spatial interactions of pixels using a finite number of parameters. It builds a
probabilistic image model that selects the most likely labeling using the
maximum a posteriori (MAP) estimation. This unsupervised approach is tested on
various images and achieves a frame-wise fire detection rate of 95.39%. Hence
this method can be used for early detection of fire in real-time and it can be
incorporated into an indoor or outdoor surveillance system.
