Publication Type

Journal Article

Version

publishedVersion

Publication Date

6-2008

Abstract

A critical issue of large-scale multimedia retrieval is how to develop an effective framework for ranking the search results. This problem is particularly challenging for content-based video retrieval due to some issues such as short text queries, insufficient sample learning, fusion of multimodal contents, and large-scale learning with huge media data. In this paper, we propose a novel multimodal and multilevel (MMML) ranking framework to attack the challenging ranking problem of content-based video retrieval. We represent the video retrieval task by graphs and suggest a graph based semi-supervised ranking (SSR) scheme, which can learn with small samples effectively and integrate multimodal resources for ranking smoothly. To make the semi-supervised ranking solution practical for large-scale retrieval tasks, we propose a multilevel ranking framework that unifies several different ranking approaches in a cascade fashion. We have conducted empirical evaluations of our proposed solution for automatic search tasks on the benchmark testbed of TRECVID2005. The promising empirical results show that our ranking solutions are effective and very competitive with the state-of-the-art solutions in the TRECVID evaluations.

Keywords

Content-based video retrieval, graph representation, multilevel ranking, multimedia retrieval, multimodal fusion, semi-supervised ranking, support vector machines

Discipline

Databases and Information Systems

Research Areas

Data Science and Engineering

Publication

IEEE Transactions on Multimedia

Volume

10

Issue

4

First Page

607

Last Page

619

ISSN

1520-9210

Identifier

10.1109/TMM.2008.921735

Publisher

IEEE

Copyright Owner and License

Authors

Additional URL

https://doi.org/10.1109/TMM.2008.921735

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