Fast image/video collection summarization with local clustering

Authors:
Shuhei Tarashima;Go Irie;Ken Tsutsuguchi;Hiroyuki Arai;Yukinobu Taniguchi
Affiliations:
NTT Corporation, Kanagawa, Japan;NTT Corporation, Kanagawa, Japan;NTT Corporation, Kanagawa, Japan;NTT Corporation, Yokosuka, Kanagawa, Japan;NTT Corporation, Yokosuka, Kanagawa, Japan
Venue:
Proceedings of the 21st ACM international conference on Multimedia
Year:
2013

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Abstract

Image/video collection summarization is an emerging paradigm to provide an overview of contents stored in massive databases. Existing algorithms require at least O(N) time to generate a summary, which cannot be applied to online scenarios. Assuming that contents are represented as a sparse graph, we propose a fast image/video collection summarization algorithm using local graph clustering. After a query node is specified, our algorithm first finds a small sub-graph near the query without looking at the whole graph, and then selects fewer number of nodes diverse to each other. Our algorithm thus provides a summary in nearly constant time in the number of contents. Experimental results demonstrate that our algorithm is more than 1500 times faster than a state-of-the-art method, with comparable summarization quality.