Boiler Flame Image Classification Based on Hidden Markov Model

The classification is an important domain in boiler flame image processing and is a preliminary step toward detection, recognition and understanding of combustion condition. In this paper, a hidden Markov model (HMM) approach is introduced into boiler flame image classification. Firstly, we define a feature vector for each flame image including 5 feature elements, which are the brightness of flame, the area of the high temperature flame, the brightness of high temperature flame, the rate of area of the high temperature flame, the offset of the flame centroid respectively. Next, for classification and recognition of the flame image, a method of the maximum posterior marginal (MPM) based on the hidden Markov random field model, which is described as a probabilistic framework for learning probability distribution defined on the sample space, is introduced. Then, we construct a sample space including 63 flame images, parts of which are used to train the model. Finally, the entire samples are recognized and classified. Experiments prove this method is effective for classification of boiler flame images