Name
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. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1145/2502081.2502189 | ACM Multimedia 2001 |
Keywords | Field | DocType |
video collection summarization,fast image,sparse graph,video collection summarization algorithm,whole graph,fewer number,local clustering,constant time,comparable summarization quality,local graph clustering,query node,summarization,multimodal,graph | Graph,Automatic summarization,Data mining,Information retrieval,Computer science,Cluster analysis,Clustering coefficient,Dense graph | Conference |
Citations | PageRank | References |
2 | 0.37 | 11 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Shuhei Tarashima | 1 | 2 | 1.04 |
| Go Irie | 2 | 299 | 20.65 |
| Ken Tsutsuguchi | 3 | 48 | 7.69 |
| Hiroyuki Arai | 4 | 64 | 13.25 |
| Yukinobu Taniguchi | 5 | 254 | 43.12 |