Abstract Developing tools to understand and visualize lifestyle is of high interest when addressing the improvement of habits and well-being of people. Routine, defined as the usual things that a person does daily, helps describe the individuals’ lifestyle. With this paper, we are the first ones to address the development of novel tools for automatic discovery of routine days of an individual from his/her egocentric images. In the proposed model, sequences of images are firstly characterized by semantic labels detected by pre-trained CNNs. Then, these features are organized in temporal-semantic documents to later be embedded into a topic models space. Finally, Dynamic-Time-Warping and Spectral-Clustering methods are used for final day routine/non-routine discrimination. Moreover, we introduce a new EgoRoutine-dataset, a collection of 104 egocentric days with more than 100.000 images recorded by 7 users. Results show that routine can be discovered and behavioural patterns can be observed.

中文翻译：

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从以自我为中心的照片流中进行日常发现的主题建模

摘要 在解决人们的习惯和福祉的改善问题时，开发工具来理解和形象化生活方式是非常重要的。常规，定义为一个人每天做的平常事情，有助于描述个人的生活方式。在这篇论文中，我们是第一个解决开发新工具的人，这些工具用于从他/她以自我为中心的图像中自动发现个人的日常工作。在所提出的模型中，图像序列首先通过预训练的 CNN 检测到的语义标签来表征。然后，这些特征被组织在时间语义文档中，以便稍后嵌入到主题模型空间中。最后，动态时间扭曲和光谱聚类方法用于最后一天的常规/非常规区分。此外，我们引入了一个新的 EgoRoutine 数据集，7 个用户记录的 104 天以自我为中心的 100.000 多张图像的集合。结果表明可以发现日常行为并观察行为模式。