Name
Abstract | ||
|---|---|---|
Temporal consistency is ubiquitous in video data, where temporally adjacent video shots usually share similar visual and semantic content.This paper presents a thorough study of temporal consistency defined with respect to semantic concepts and query topics using quantitative measures,and discusses its implications to video analysis and retrieval tasks. We further show that,in interactive settings, using temporal consistency leads to considerable improvement on the performance of semantic concept detection and retrieval of video data.Speci fically,an active learning method with temporal sampling strategy is proposed for building classifiers of semantic concepts,and a temporal reranking method is proposed for improving the efficiency of interactive video search.Both methods outperform existing methods by considerable margins on the TRECVID dataset. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1178677.1178685 | Multimedia Information Retrieval |
Keywords | Field | DocType |
temporal reranking method,temporal consistency,temporally adjacent video shot,video data,video analysis,interactive video search,temporal sampling strategy,semantic content,semantic concept detection,semantic concept,active learning | Interactive video,Active learning,Video retrieval,Information retrieval,TRECVID,Computer science,Video tracking,Sampling (statistics),Interactive search,Temporal consistency | Conference |
ISBN | Citations | PageRank |
1-59593-495-2 | 33 | 1.24 |
References | Authors | |
15 | 2 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jun Yang | 1 | 937 | 37.42 |
| Alexander G. Hauptmann | 2 | 7472 | 558.23 |