Non-Linear Dimensionality Reduction and Gaussian Process Based Classification Method for Smoke Detection

Abstract

To improve smoke detection accuracy, we combine local binary pattern (LBP) like features, kernel principal component analysis (KPCA), and Gaussian process regression (GPR) to propose a novel data processing pipeline for smoke detection. The data processing pipeline consists of three steps including original feature extraction, dimensionality reduction, and classification. We use LBP-like methods to extract original features. To obtain a more discriminant feature, KPCA is used to non-linearly map the original features into a discriminant feature space, where manifold structures are embedded. Finally, in order to improve generalization performance, we apply GPR to model classification as a Gaussian process by imposing Gaussian priors on both data and hyper-parameters. In addition, we can replace any steps of the pipeline by similar methods for further improvement or exploration, so the pipeline is flexible and extensible. Experimental results show that KPCA and GPR are truly able to improve the performance of smoke detection and texture classification, and our method obviously outperforms the same features with Support Vector Machine (SVM).