Mining near-duplicate graph for cluster-based reranking of web video search results

Huang, Zi, Hu, Bo, Cheng, Hong, Shen, Heng Tao, Liu, Hongyan and Zhou, Xiaofang (2010) Mining near-duplicate graph for cluster-based reranking of web video search results. ACM Transactions on Information Systems, 28 4: 22-1-22-27. doi:10.1145/1852102.1852108

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Author Huang, Zi
Hu, Bo
Cheng, Hong
Shen, Heng Tao
Liu, Hongyan
Zhou, Xiaofang
Title Mining near-duplicate graph for cluster-based reranking of web video search results
Journal name ACM Transactions on Information Systems   Check publisher's open access policy
ISSN 1046-8188
Publication date 2010-11
Sub-type Article (original research)
DOI 10.1145/1852102.1852108
Volume 28
Issue 4
Start page 22-1
End page 22-27
Total pages 27
Place of publication New York, United States
Publisher Association for Computing Machinery (ACM)
Collection year 2011
Language eng
Abstract Recently, video search reranking has been an effective mechanism to improve the initial text-based ranking list by incorporating visual consistency among the result videos. While existing methods attempt to rerank all the individual result videos, they suffer from several drawbacks. In this article, we propose a new video reranking paradigm called cluster-based video reranking (CVR). The idea is to first construct a video near-duplicate graph representing the visual similarity relationship among videos, followed by identifying the near-duplicate clusters from the video near-duplicate graph, then ranking the obtained near-duplicate clusters based on cluster properties and intercluster links, and finally for each ranked cluster, a representative video is selected and returned. Compared to existing methods, the new CVR ranks clusters and exhibits several advantages, including superior reranking by utilizing more reliable cluster properties, fast reranking on a small number of clusters, diverse and representative results. Particularly, we formulate the near-duplicate cluster identification as a novel maximally cohesive subgraph mining problem. By leveraging the designed cluster scoring properties indicating the cluster's importance and quality, random walk is applied over the near-duplicate cluster graph to rank clusters. An extensive evaluation study proves the novelty and superiority of our proposals over existing methods.
Keyword Design
Cluster-based video reranking
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ
Additional Notes Article number 22, pp. 1-27

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2011 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 4 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 13 times in Scopus Article | Citations
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Created: Tue, 16 Nov 2010, 14:16:56 EST by Dr Heng Tao Shen on behalf of School of Information Technol and Elec Engineering