| 中文简介: |
1995年, 皮卡尔提出了如何将情感计算用于多媒体选择的想法 [1]。她设想了一个内容玩家, 它可以感知用户的情绪状态, 并提供与她的情绪状态相匹配的内容。这也需要对内容本身的情感理解。2001年, 韩雅利奇和徐提出了一种面向用户的情感视频内容分析, 该分析开创了研究的轨迹, 旨在利用内容了解视频的情感内容 [2]。 随着目前用户生成内容的扩展速度的加快。经典的认知索引方法显示了它们的局限性。情感索引显示了一个潜在的替代方案, 吸引了多媒体研究人员。用户还期待着能够更好地适应他们的口味和情绪的内容推荐和交付系统。尽管用户互动和社会信息正在弥合用户与机器之间现有的差距, 但来自内容和用户的情感理解肯定会改善用户的体验。 虽然, 情感计算现在有自己的期刊, ieee 交易的情感计算, 其一年两次的会议, 情感计算和智能交互 (acii) 多媒体社区没有强大的存在, 在这些出版物。多媒体相关情感研究正在不同的场所出版, 缺乏连贯和规范。与情感识别研究不同的是, 情绪识别研究具有大量可公开使用的数据库和挑战。视频情感分析缺乏标准基准。这在一定程度上是由于使用了受版权保护的材料, 禁止发布和共享数据集。这种缺乏共识的另一个原因是, 这种研究轨道缺乏自己的论坛, 将感兴趣的学者或工业关键角色聚集在一起。在本次演讲中, 我将介绍在内容交付系统中使用影响的想法的起源, 从 picard 的技术报告, 并关注它在过去十年中的发展到它目前的发展现状。讲座的重点将是情感表征的内容分析, 而不是影响感知。最后, 我将对情感语料库的发展提出建议, 并给出一个公共情感内容语料库发展的例子, 即中世纪基准运动中的暴力场景检测。 参考: \ \ [1] 皮卡尔, r. (1995)。情感计算。技术报告 321, 麻省理工学院媒体实验室, 麻省理工学院媒体实验室: 感知计算;20 ames st., cambridge, ma 02139。[2] 汉雅利奇, a;徐立群, "面向用户的情感视频内容分析," 基于内容的图像和视频库访问, 2001年。(cbaivl, 2001年)。ieee 讲习班, pp.50‐57,, doi: 10.1109/IVL.2001.990856,
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| 课程简介: |
In 1995, Picard proposed ideas about how to use affective computing for multimedia selection [1]. She envisaged a content player which can sense user’s emotional state and deliver the content that matches her emotional state. This also needs an emotional understanding of the content itself. In 2001, Hanjalic and Xu proposed a user oriented affective video content analysis which pioneered the track of research which aimed at understanding the affective content of videos using the content [2]. With the current rate of the expansion of user generated content. The classic, cognitive indexing methods are showing their limits. Affective indexing is showing a potential alternative which attracts multimedia researchers. Users are also expecting content recommendation and delivery systems that can better adapt to their taste and emotions. Although the user interaction and social information is bridging the existing gap between the users and machines, emotional understanding from the content and users will certainly improve users’ experience. Although, affective computing now has its own journal, IEEE Transactions on Affective Computing, and its biannual conference, Affective Computing and Intelligent Interactions (ACII) multimedia community does not have a strong presence in those publications. Multimedia related affective research is being published in different venues and lacks coherence and standardization. Unlike, emotion recognition studies which have large number of publicly available databases and challenges. There is a lack of standard benchmarks for video affective analysis. This is partly due to the usage of copyrighted material which prohibits publishing and sharing the datasets. The other reason behind this lack of consensus is that this track of research lacks its own forum which brings together the interested scholars or industrial key players. In this talk, I will present the origins of the idea of using affect in content delivery system, from Picard’s technical report and follow its development in the last decade to its current state of the art. The focus of the talk will be on content analysis for affective characterization and not on affect sensing. At the end, I will give recommendations on affective corpora development and present an example of public affective content corpus development, i.e., Violence scenes detection at Mediaeval benchmarking campaign. References:\\ [1] Picard, R. (1995). Affective computing. Technical Report 321, MIT Media Laboratory, MIT Media Laboratory: Perceptual Computing; 20 Ames St., Cambridge, MA 02139. [2] Hanjalic, A.; Li‐Qun Xu, "User‐oriented affective video content analysis," Content‐Based Access of Image and Video Libraries, 2001. (CBAIVL 2001). IEEE Workshop on , vol., no., pp.50‐57, 2001.doi: 10.1109/IVL.2001.990856,
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