Local binary pattern based texture analysis for visual fire recognition

Color and shape features have difficulty to recognize fire from fire-colored, irregular-shaped and moving objects. An effective algorithm using texture analysis with the Local Binary Pattern (LBP) is proposed in our paper to help deal with this problem. Besides grayscale and rotation invariance, the ability of LBP operator for multi-resolution analysis is enhanced through multiple LBP operators with varying parameters and pyramid based image sub-sampling. Experiments are performed on sample pictures of red fire, car and banner taking Mahalanobis distance classifier as the evaluation criterion. From experimental results the principles are analyzed for the selections of fire region, color channel and histogram equalization. To illustrate the efficiency of our adopted LBP method, its performance has been compared with those from Gray Level Co-occurrence Matrices (GLCM) and wavelet transform (WT).