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IEEE TRANSACTIONS ON MULTIMEDIA 1 A Multimodal and Multilevel Ranking Scheme for Large-Scale Video Retrieval
| Content Provider | CiteSeerX |
|---|---|
| Author | Lyu, Michael R. Hoi, Steven C. H. |
| Abstract | 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. Index Terms—Content-based video retrieval, graph representation, multilevel ranking, multimodal fusion, multimedia retrieval, semi-supervised ranking, support vector machines. I. |
| File Format | |
| Access Restriction | Open |
| Subject Keyword | Benchmark Testbed Semi-supervised Ranking Multilevel Ranking Scheme Huge Medium Data Video Retrieval Task Multimodal Fusion Support Vector Machine Search Result Cascade Fashion Graph Representation Large-scale Learning Content-based Video Retrieval Ranking Solution Index Term Content-based Video Retrieval Multimedia Retrieval Ranking Problem Critical Issue Trecvid Evaluation State-of-the-art Solution Multimodal Resource Short Text Query Semi-supervised Ranking Solution Practical Novel Multimodal Large-scale Video Retrieval Large-scale Multimedia Retrieval Promising Empirical Result Empirical Evaluation Ieee Transaction Multimedia Effective Framework Multimodal Content Insufficient Sample Learning Automatic Search Task Large-scale Retrieval Task Multilevel Ranking Small Sample |
| Content Type | Text |
