A multimodal and multilevel ranking scheme for large-scale video retrieval
Publication in refereed journal


摘要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.
著者Hoi SCH, Lyu MR
期刊名稱IEEE Transactions on Multimedia
出版社Institute of Electrical and Electronics Engineers (IEEE)
頁次607 - 619
關鍵詞content-based video retrieval; graph representation; multilevel ranking; multimedia retrieval; multimodal fusion; semi-supervised ranking; support vector machines
Web of Science 學科類別Computer Science; Computer Science, Information Systems; COMPUTER SCIENCE, INFORMATION SYSTEMS; Computer Science, Software Engineering; COMPUTER SCIENCE, SOFTWARE ENGINEERING; Telecommunications; TELECOMMUNICATIONS

上次更新時間 2021-10-01 於 00:48