Efficient Tracking of News Topics Based on Chronological Semantic Structures in a Large-Scale News Video Archive
スポンサーリンク
概要
- 論文の詳細を見る
Recent advance in digital storage technology has enabled us to archive a large volume of video data. Thanks to this trend, we have archived more than 1,800 hours of video data from a daily Japanese news show in the last ten years. When considering the effective use of such a large news video archive, we assumed that analysis of its chronological and semantic structure becomes important. We also consider that providing the users with the development of news topics is more important to help their understanding of current affairs, rather than providing a list of relevant news stories as in most of the current news video retrieval systems. Therefore, in this paper, we propose a structuring method for a news video archive, together with an interface that visualizes the structure, so that users could track the development of news topics according to their interest, efficiently. The proposed news video structure, namely the “topic thread structure”, is obtained as a result of an analysis of the chronological and semantic relation between news stories. Meanwhile, the proposed interface, namely “mediaWalker II”, allows users to track the development of news topics along the topic thread structure, and at the same time watch the video footage corresponding to each news story. Analyses on the topic thread structures obtained by applying the proposed method to actual news video footages revealed interesting and comprehensible relations between news topics in the real world. At the same time, analyses on their size quantified the efficiency of tracking a user's topic-of-interest based on the proposed topic thread structure. We consider this as a first step towards facilitating video authoring by users based on existing contents in a large-scale news video archive.
著者
-
Takahashi Tomokazu
Faculty Of Economics And Information Gifu Shotoku Gakuen University
-
Mo Hiroshi
National Institute of Informatics
-
Murase Hiroshi
Graduate School Of Information Science Nagoya University
-
Ide Ichiro
Graduate School Of Information Science Nagoya University
-
Kinoshita Tomoyoshi
Netcompass Ltd.
-
SATOH Shin'ichi
National Institute of Informatics, Research Organization of Information and Systems
-
KATAYAMA Norio
National Institute of Informatics, Research Organization of Information and Systems
-
TAKAHASHI Tomokazu
Faculty of Economics and Information, Gifu Shotoku Gakuen University
関連論文
- Combining Three Different Types of Local Features for Generic Object Recognition(International Session 1)
- Incremental Unsupervised-Learning of Appearance Manifold with View-Dependent Covariance Matrix for Face Recognition from Video Sequences
- Construction of Appearance Manifold with Embedded View-Dependent Covariance Matrix for 3D Object Recognition
- Generation of Training Data by Degradation Models for Traffic Sign Symbol Recognition(Image Recognition and Understanding)
- Appearance Manifold with Covariance Matrix for 3-D Object Recognition(CV)
- A Study of Mach--Zehnder Interferometer Type Optical Modulator Applicable to an Accelerometer
- Improvement of face identification by using image sequence (パターン認識・メディア理解)
- FREE IRIS AND FOCUS IMAGE GENERATION BY MERGING MULTIPLE DIFFERENTLY FOCUSED IMAGES IN THE THREE-DIMENSIONAL FREQUENCY DOMAIN(International Workshop on Advanced Image Technology 2006)
- Face recognition based on virtual frontal view generation using LVTM with local patches clustering (パターン認識・メディア理解)
- Face recognition based on virtual frontal view generation using LVTM with local patches clustering
- Efficient Tracking of News Topics Based on Chronological Semantic Structures in a Large-Scale News Video Archive
- Efficient Tracking of News Topics Based on Chronological Semantic Structures in a Large-Scale News Video Archive
- Face recognition based on virtual frontal view generation using LVTM with local patches clustering
- Cross-Pose Face Recognition — A Virtual View Generation Approach Using Clustering Based LVTM