本文是对1980年代以来英文传播类期刊里的中国媒体内容分析研究的整理。自1980年代中国大陆门户重开以来,中国媒体内容分析渐成海外中国传媒研究的两大取径之一。Journal of Communication和Harvard International Journal of Press/Pol...本文是对1980年代以来英文传播类期刊里的中国媒体内容分析研究的整理。自1980年代中国大陆门户重开以来,中国媒体内容分析渐成海外中国传媒研究的两大取径之一。Journal of Communication和Harvard International Journal of Press/Politics等SSCI传播类期刊上出现了不少剖析中国传媒所呈现的世界的论文。我们认为,考察它们的来龙去脉,追问它们的主题、方法和意义,不仅能为我们反观中国大陆的中国媒体内容分析研究提供借鉴,更为我们理解关于中国传媒的知识生产中的跨文化现象提供一个相对新颖的视角。展开更多
The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the ...The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systelrtically investigated, and kernelbased learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systen on both classifier-level and kernel-level fusion that contribute to a more robust system Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision (MAP) score is rmch better than the benchmark perforrmnce, which proves that our concepts detection engine develops a generic model and perforrrs well on both object and scene type concepts.展开更多
文摘本文是对1980年代以来英文传播类期刊里的中国媒体内容分析研究的整理。自1980年代中国大陆门户重开以来,中国媒体内容分析渐成海外中国传媒研究的两大取径之一。Journal of Communication和Harvard International Journal of Press/Politics等SSCI传播类期刊上出现了不少剖析中国传媒所呈现的世界的论文。我们认为,考察它们的来龙去脉,追问它们的主题、方法和意义,不仅能为我们反观中国大陆的中国媒体内容分析研究提供借鉴,更为我们理解关于中国传媒的知识生产中的跨文化现象提供一个相对新颖的视角。
基金Acknowledgements This paper was supported by the coUabomtive Research Project SEV under Cant No. 01100474 between Beijing University of Posts and Telecorrrcnications and France Telecom R&D Beijing the National Natural Science Foundation of China under Cant No. 90920001 the Caduate Innovation Fund of SICE, BUPT, 2011.
文摘The rapid growth of multimedia content necessitates powerful technologies to filter, classify, index and retrieve video documents more efficiently. However, the essential bottleneck of image and video analysis is the problem of semantic gap that low level features extracted by computers always fail to coincide with high-level concepts interpreted by humans. In this paper, we present a generic scheme for the detection video semantic concepts based on multiple visual features machine learning. Various global and local low-level visual features are systelrtically investigated, and kernelbased learning method equips the concept detection system to explore the potential of these features. Then we combine the different features and sub-systen on both classifier-level and kernel-level fusion that contribute to a more robust system Our proposed system is tested on the TRECVID dataset. The resulted Mean Average Precision (MAP) score is rmch better than the benchmark perforrmnce, which proves that our concepts detection engine develops a generic model and perforrrs well on both object and scene type concepts.