Unsupervised temporal ensemble alignment for rapid annotation
Fagg, Ashton, Sridharan, Sridha, & Lucey, Simon (2014) Unsupervised temporal ensemble alignment for rapid annotation. In The 12th Asian Conference on Computer Vision (ACCV 2014), 1-5 November 2014, Singapore.
This paper presents a novel framework for the unsupervised alignment of an ensemble of temporal sequences. This approach draws inspiration from the axiom that an ensemble of temporal signals stemming from the same source/class should have lower rank when "aligned" rather than "misaligned". Our approach shares similarities with recent state of the art methods for unsupervised images ensemble alignment (e.g. RASL) which breaks the problem into a set of image alignment problems (which have well known solutions i.e. the Lucas-Kanade algorithm). Similarly, we propose a strategy for decomposing the problem of temporal ensemble alignment into a similar set of independent sequence problems which we claim can be solved reliably through Dynamic Time Warping (DTW). We demonstrate the utility of our method using the Cohn-Kanade+ dataset, to align expression onset across multiple sequences, which allows us to automate the rapid discovery of event annotations.
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|Item Type:||Conference Paper|
|Divisions:||Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Springer Verlag|
|Deposited On:||11 Nov 2014 23:25|
|Last Modified:||04 May 2015 08:26|
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