Video News Retrieval Incorporating Relevant Terms Based on Distribution of Document Frequency
| 详细信息 | 推荐本文 | |
- 作者：Jun-Bin Yeh ; Chung-Hsien Wu
- 关键词：Relevant event term ; Video retrieval ; boxplot ; TFIDF ; Vector Space Model ; Term Extraction
- 刊名：Lecture Notes in Computer Science
- 出版时间：2008
- 出版年：2008
- 期刊代码：99_0302-9743
- 类别：cp
- 卷：5353
- 期：1
- 页码：583-592
- 数据来源：sp
摘要
This paper presents an approach to video news retrieval within an event by integrating visual and textual features. A set of histogram bins of key frames in a shot is adopted as the visual feature, while the term frequency is used as the textual feature. A term scoring method is proposed to enhance the weights of relevant terms in an event by considering the windowed document frequency distribution. The weight for a given term is determined by mean of the difference between usual and unusual term groups which are quantized by the boxplot method. The first experiment evaluate the performance of the proposed method by giving generated document frequency distributions, while the second experiment gives the desired retrieval results for relevant terms in the real data. It concludes the proposed method can increase the performance of retrieving video news stories within an event using relevant terms.