Extracting Texture Features for Time Series Classification
22nd International Conference on Pattern Recognition (ICPR2014)

Vinícius M. A. de Souza, Diego F. Silva, Gustavo E. A. P. A. Batista

Abstract Time series are present in many pattern recognition applications related to medicine, biology, astronomy, economy, and others. In particular, the classification task has attracted much attention from a large number of researchers. In such a task, empirical researches has shown that the 1-Nearest Neighbor rule with a distance measure in time domain usually performs well in a variety of application domains. However, certain time series features are not evident in time domain. A classical example is the classification of sound, in which representative features are usually present in the frequency domain. For these applications, an alternative representation is necessary. In this work we investigate the use of recurrence plots as data representation for time series classification. This representation has well-defined visual texture patterns and their graphical nature exposes hidden patterns and structural changes in data. Therefore, we propose a method capable of extracting texture features from this graphical representation, and use those features to classify time series data. We use traditional methods such as Grey Level Co-occurrence Matrix and Local Binary Patterns, which have shown good results in texture classification. In a comprehensible experimental evaluation, we show that our method outperforms the state-of-the-art methods for time series classification.

Keywords time series classification; recurrence plots; texture features;

Contacts {vsouza, diegofsilva, gbatista}@icmc.usp.br

Texture Features from Recurrence Patterns (TFRP)

Overview of proposed method

Overview of proposed method
Available files


The time series in time domain is available in UCR Repository:
UCR Time Series Classification/Clustering Page

To transform the series from the time domain to recurrence plot representation, you can use the follow code (Matlab):
Time series in recurrence plot representation

Source codes

To extract the LBP texture features, you can use the code available at:
University of Oulu (Center for Machine Vision Research)

To extract the GLCM texture features, you can use the code available at:
Matlab central (Avinash Uppuluri profile)

To extract the Gabor texture features, you can use the code available at:
Matlab central (Manohar profile)

To extract the SFTA texture features, you can use the code available at:
Matlab central (Alceu Costa profile)

Detailed results

All informations about our results in ARFF format can be download:


Other links

ICPR 2014

The website of the 22nd International Conference on Pattern Recognition