15 research outputs found
Classification of Time-Series Images Using Deep Convolutional Neural Networks
Convolutional Neural Networks (CNN) has achieved a great success in image
recognition task by automatically learning a hierarchical feature
representation from raw data. While the majority of Time-Series Classification
(TSC) literature is focused on 1D signals, this paper uses Recurrence Plots
(RP) to transform time-series into 2D texture images and then take advantage of
the deep CNN classifier. Image representation of time-series introduces
different feature types that are not available for 1D signals, and therefore
TSC can be treated as texture image recognition task. CNN model also allows
learning different levels of representations together with a classifier,
jointly and automatically. Therefore, using RP and CNN in a unified framework
is expected to boost the recognition rate of TSC. Experimental results on the
UCR time-series classification archive demonstrate competitive accuracy of the
proposed approach, compared not only to the existing deep architectures, but
also to the state-of-the art TSC algorithms.Comment: The 10th International Conference on Machine Vision (ICMV 2017
Pattern Recognition
A wealth of advanced pattern recognition algorithms are emerging from the interdiscipline between technologies of effective visual features and the human-brain cognition process. Effective visual features are made possible through the rapid developments in appropriate sensor equipments, novel filter designs, and viable information processing architectures. While the understanding of human-brain cognition process broadens the way in which the computer can perform pattern recognition tasks. The present book is intended to collect representative researches around the globe focusing on low-level vision, filter design, features and image descriptors, data mining and analysis, and biologically inspired algorithms. The 27 chapters coved in this book disclose recent advances and new ideas in promoting the techniques, technology and applications of pattern recognition