Novelty Detection Using Level Set Methods with Adaptive Boundaries

This paper proposes a locally adaptive level set boundary description (LALSBD) method for novelty detection. The proposed method adjusts the nonlinear boundary directly in the input space and consists of a number of processes including level set function (LSF) construction, local boundary evolution and termination. It employs kernel density estimation (KDE) to construct the LSF and form the initial boundary surrounding the training data. In order to make the boundary better fit the data distribution, a data-driven based local expanding/shrinking evolution method is proposed instead of the global evolution approach reported in our previous level set boundary description (LSBD) method. The proposed LALSBD is compared with LSBD and other four representative novelty detection methods. The experimental results demonstrate that LALSBD can detect novel events more accurately, especially for applications which demand very high classification accuracy for normal events.