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Toward storytelling from personal informative lifelogging

机译:从个人信息丰富的生活中讲故事

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The authors began collecting personal informative lifelogging data in 2011. The data collected in this article is a discontinuous data set, each of which includes one or more images, a GPS message, a description of a location, and a description of the text, we call this informative lifelogging. However, for the purposes automatically building a story from a huge collection of unstructured egocentric data presents major challenges. This paper first introduces the structure and characteristics of the collected data and uses the DB-scan algorithm to classify the data. Then a model for generating a story is proposed, and a model of story generation based on a story template is proposed in the model. The author implemented a complete software system through code, described a story generation model, and gave the key algorithm to generate stories. Through this system, 418 stories were generated automatically, of which 62% of the stories were particularly accurate. The experimental results verify that it is feasible to automatically generate stories based on personal Informative lifelogging data.
机译:作者开始于2011年收集个人信息的生命线数据。本文收集的数据是一个不连续的数据集,每个数据集包括一个或多个图像,GPS消息,位置的描述,以及文本的描述,以及文本的描述,我们致电这种信息生活。然而,为了自动构建一个来自巨大的非结构化的Enocentric数据的故事提出了重大挑战。本文首先介绍了收集数据的结构和特性,并使用DB-Scan算法对数据进行分类。然后提出了一种生成故事的模型,并且在模型中提出了一种基于故事模板的故事生成模型。作者通过代码实现了一个完整的软件系统,描述了一个故事生成模型,并给出了一个键生成故事的关键算法。通过这个系统,自动产生418个故事,其中62%的故事特别准确。实验结果验证了自动生成基于个人信息的LifeLogging数据是可行的。

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