Deriving image features for autonomous classification from time-series recurrence plots

Abstract

This paper shows the use of a specific type of time series analyses, the so named recurrence plot (RP), for investigations of the outer hull of an imaged and pre-segmented object to derive image features suitable for usage in classificators. Additionally to the features derived by the well documented recurrence quantification analysis (RQA) a new set of features was developed based on closed structures (“eyes”) in a RP. The new features were named eye structure quantification (ESQ). Two sets of images are analysed: a) 1023 in-situ plankton images comprising nine different organism classes, and b) each 50 algorithmically created geometric shapes of five different classes. These images were characterised by standard image features, RQA quantification and the newly proposed features. A Linear Discriminant Analysis (LDA) was used to determine discriminative success between the classes of plankton organisms or geometric shapes respectively. The discriminative success was compared between a model using standard features and additional RQA and ESQ. For the high intra- and low interclass variance of the plankton contour line data set the included features enhanced discriminative success by 3 % to a maximum of 65.8 %. For the data set of geometric shapes an increase of 6.8 % to 95.2 % was observed. Although the overall increase of discriminative success was not extraordinary high by using a linear model, it can be seen that both RQA and ESQ are valuable auxiliary features to split specific classes from the entire population. Thus, they may also be valuable for methods mapping the finite dimensional feature space into higher dimensional spaces (e.g. Kernel trick, Support Vector Machines).