Structuring continuous video recordings of everyday life using time-constrained clustering

Wei-Hao Lin; Alexander Hauptmann

doi:10.1117/12.642009

16 January 2006 Structuring continuous video recordings of everyday life using time-constrained clustering

Wei-Hao Lin, Alexander Hauptmann

Author Affiliations +

Proceedings Volume 6073, Multimedia Content Analysis, Management, and Retrieval 2006; 60730D (2006) https://doi.org/10.1117/12.642009
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

As personal wearable devices become more powerful and ubiquitous, soon everyone will be capable to continuously record video of everyday life. The archive of continuous recordings need to be segmented into manageable units so that they can be efficiently browsed and indexed by any video retrieval systems. Many researchers approach the problem in two-pass methods: segmenting the continuous recordings into chunks, followed by clustering chunks. In this paper we propose a novel one-pass algorithm to accomplish both tasks at the same time by imposing time constraints on the K-Means clustering algorithm. We evaluate the proposed algorithm on 62.5 hours of continuous recordings, and the experiment results show that time-constrained clustering algorithm substantially outperforms the unconstrained version.

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Wei-Hao Lin and Alexander Hauptmann "Structuring continuous video recordings of everyday life using time-constrained clustering", Proc. SPIE 6073, Multimedia Content Analysis, Management, and Retrieval 2006, 60730D (16 January 2006); https://doi.org/10.1117/12.642009

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