K-autoregressive clustering: Application on terahertz image analysis

Abstract

In this paper, we propose to segment Terahertz (THz) images and introduce a new family of clustering based regression techniques suitable to time series. In particular, we propose a novel approach called K Autoregressive (K-AR) model in which we assume that the time series depicting the pixels were generated by AR models. The K-AR model consists to minimize a new objective function for recovering the original K autoregressive models describing each cluster of time series. The corresponding pixels are then assigned to the clusters having the best AR model fitting. The order of K-AR model is automatically estimated using a model selection criterion. Our algorithm is tested on two real THz images. Comparison with existing clustering algorithms shows the efficiency of the proposed approach.