The proliferation of portable and even wearable visual sensing devices e.g. SenseCam, Google Glass, etc. is creating opportunities for automatic indexing and management of digitally-recorded everyday behaviour. Although the detection of semantic concepts within narrow domains has now reached a satisfactory performance level based on automatic mapping from low-level features to higher level semantics, in wearable sensing and life-logging, a diversity of everyday concepts are captured by the images and this challenges the performance of automatic concept detection and activity indexing based on this. In this paper, we investigated and compared factorization methods in utilising the semantics of concept re-occurrence and co-occurrence patterns. The factorized results are then input to activity recognition to show the efficacies in enhancing recognition performances.

Date Published: 29 August 2016
PDF: 5 pages
Proc. SPIE 10033, Eighth International Conference on Digital Image Processing (ICDIP 2016), 100330X (29 August 2016); doi: 10.1117/12.2243847

Published in SPIE Proceedings Vol. 10033:
Eighth International Conference on Digital Image Processing (ICDIP 2016)
Charles M. Falco; Xudong Jiang, Editor(s)