  Time-Series Classification (TSC) has attracted a lot of attention in pattern
recognition, because wide range of applications from different domains such as
finance and health informatics deal with time-series signals. Bag of Features
(BoF) model has achieved a great success in TSC task by summarizing signals
according to the frequencies of "feature words" of a data-learned dictionary.
This paper proposes embedding the Recurrence Plots (RP), a visualization
technique for analysis of dynamic systems, in the BoF model for TSC. While the
traditional BoF approach extracts features from 1D signal segments, this paper
uses the RP to transform time-series into 2D texture images and then applies
the BoF on them. Image representation of time-series enables us to explore
different visual descriptors that are not available for 1D signals and to
treats TSC task as a texture recognition problem. Experimental results on the
UCI time-series classification archive demonstrates a significant accuracy
boost by the proposed Bag of Recurrence patterns (BoR), compared not only to
the existing BoF models, but also to the state-of-the art algorithms.
