Incremental Graph Clustering for Efficient Retrieval from Streaming Egocentric Video Data
With wearable devices like Google Glass, it will soon become possible to record everything we see. We envision a system where one's entire visual memory is captured, stored and indexed. One of the biggest challenges is the scale of the retrieval problem. In this work, we focus on how to organize streaming egocentric video data. Egocentric video data is highly redundant, in that, we see several objects and scenes repeatedly as we go about our lives. To exploit this redundancy, we propose an evolving sparse-graph representation for egocentric video data. We propose an incremental local density clustering scheme, which learns salient objects and scenes for streaming egocentric video data. We use the density clustering scheme to prune redundant data in the database. For image-retrieval applications, by retaining only representative nodes from dense sub graphs in the streaming data source, we show we can achieve 90% of peak recall by retaining only 1% of data, with a significant 18% improvement in absolute recall over naive uniform sub sampling of the egocentric video data.