  Colour analysis is a crucial step in image-based fire detection algorithms.
Many of the proposed fire detection algorithms in a still image are prone to
false alarms caused by objects with a colour similar to fire. To design a
colour-based system with a better false alarm rate, a new
colour-differentiating conversion matrix, efficient on images of high colour
complexity, is proposed. The elements of this conversion matrix are obtained by
performing K-medoids clustering and Particle Swarm Optimisation procedures on a
fire sample image with a background of high fire-colour similarity. The
proposed conversion matrix is then used to construct two new fire colour
detection frameworks. The first detection method is a two-stage non-linear
image transformation framework, while the second is a direct transformation of
an image with the proposed conversion matrix. A performance comparison of the
proposed methods with alternate methods in the literature was carried out.
Experimental results indicate that the linear image transformation method
outperforms other methods regarding false alarm rate while the non-linear
two-stage image transformation method has the best performance on the F-score
metric and provides a better trade-off between missed detection and false alarm
rate.
