# A Fire Detection Algorithm Based on Tchebichef Moment Invariants and PSO-SVM
## 1 Introduction
Besides, some color spaces have the disadvantage of illumination dependence. In other words, the changes of illumination in the image have a negative effect on the fire pixel classification rules. [PERSON] et al. [9] proposed a method for automatic fire smoke detection based on image visual features. This was achieved by extracting the static and dynamic features of fire smoke, including growth, disorder, frequent flicker in boundaries, self-similarity, and local wavelet energy. In Reference [10], a statistical analysis was carried out to detect smoke, using the idea that smoke is characterized by a grayish color with different illuminations. However, these smoke-based fire detection methods can give false alarms if proper consideration is not taken into account. Great challenges are yet to be overcome, such as dealing with smoke produced by cigarettes or friendly smoke. Several researchers have theoretically investigated the Gaussian mixture model [11; 12] and optical flow method [13; 14], which are suitable for fire image detection. However, these methods also have some drawbacks, such as relying on a background frame and requiring huge computational resources. From the above discussion, there is no doubt that a novel and effective algorithm for the feature extraction of fire images is needed. As for state detection, artificial neural network (ANN) has been widely used for fire image detection, which is a special case of neural computation [15; 16; 17]. It applies historical data and has strong robustness of data analysis, achieving a better detection rate than the conventional algorithms. Nevertheless, the negative aspects of over-fitting, slow convergence rate, and becoming easily trapped in the local optimal solution are also obvious. Support vector machine (SVM) is a machine learning method based on statistical theory, which was first put forward by [PERSON] [18]. It can overcome the dimensionality disaster and guarantee that the local and global optimal solution are exactly the same. SVM minimizes the upper bound of the generalization error based on structural risk minimization (SRM) and considers the empirical risk minimization (ERM) simultaneously, havinga better generalization performance than ANN [19; 20; 21; 22]. Meanwhile, it has a smaller demand for samples [23; 24]. Many studies have been published concerning the state classification of fire with SVM [25; 26; 27; 28], but little attention has been focused on how to select its parameters. It is well known that the performance of SVM greatly depends on its parameters. However, due to the lack of theoretical guidance, the choice of the parameters mostly relies on experience and manual selection in practical applications, which is troublesome and time-consuming [29; 30; 31]. In order to improve the fire detection performance and work efficiency, it is necessary to apply an effective and intelligent method to obtain the optimal parameters of SVM. In this paper, a novel fire image detection algorithm is proposed through combining Tchebichef moment invariants (TMIs) and particle swarm optimization-support vector machine (PSO-SVM). Firstly, the translation, rotation, and scaling (TRS) invariants of Tchebichef moments are obtained in terms of the geometric moments. Then, the features of candidate images are extracted by calculating their TMIs to form feature vectors. To gain the best performance of fire detection, a PSO-SVM model is built with the feature vectors of the training sample set, to which the PSO is applied to search the best kernel parameter and penalty factor of SVM. Afterwards, the PSO-SVM model is used to detect the fire images of the testing sample set. To evaluate the performance of the proposed algorithm, a comparison with the algorithms based on HMIs and ZMIs is also performed. Finally, the experimental results show that the proposed algorithm can improve the detection accuracy, achieving the highest detection rate of 98.18%.