Segment-Based Features for Time Series Classification

Publisher:
Oxford Press
Publication Type:
Journal Article
Citation:
Computer Journal, 2012, 55 (9), pp. 1088 - 1102
Issue Date:
2012-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2011007959OK.pdf928.72 kB
Adobe PDF
In this paper, we propose an approach termed segment-based features (SBFs) to classify time series. The approach is inspired by the success of the component- or part-based methods of object recognition in computer vision, in which a visual object is described as a number of characteristic parts and the relations among the parts. Utilizing this idea in the problem of time series classification, a time series is represented as a set of segments and the corresponding temporal relations. First, a number of interest segments are extracted by interest point detection with automatic scale selection. Then, a number of feature prototypes are collected by random sampling from the segment set, where each feature prototype may include single segment or multiple ordered segments. Subsequently, each time series is transformed to a standard feature vector, i.e. SBF, where each entry in the SBF is calculated as the maximum response (maximum similarity) of the corresponding feature prototype to the segment set of the time series.
Please use this identifier to cite or link to this item: