论文标题
从以自我为中心的照片流的收集中发现行为模式
Behavioural pattern discovery from collections of egocentric photo-streams
论文作者
论文摘要
在评估和改善人们的生活质量时，自动发现行为至关重要。以自我为中心的图像对摄像头佩戴者的日常生活提供了丰富而客观的描述。这项工作提出了一种新方法，以从收集的以自我为中心的照片流中识别一个人的行为模式。我们的模型根据定义图像组成的上下文（地点，活动和环境对象）来表征时间框架。基于描述用户收集到的天数的时间框架之间的相似性，我们提出了一种新的无监督贪婪方法，以根据一种新颖的语义聚类方法来发现设置的行为模式。此外，我们提出了一个新的分数指标，以评估所提出的算法的性能。我们在104天内验证我们的方法，并从7个用户中提取了超过100k图像。结果表明，可以发现行为模式以表征个人的常规，从而表征其生活方式。
The automatic discovery of behaviour is of high importance when aiming to assess and improve the quality of life of people. Egocentric images offer a rich and objective description of the daily life of the camera wearer. This work proposes a new method to identify a person's patterns of behaviour from collected egocentric photo-streams. Our model characterizes time-frames based on the context (place, activities and environment objects) that define the images composition. Based on the similarity among the time-frames that describe the collected days for a user, we propose a new unsupervised greedy method to discover the behavioural pattern set based on a novel semantic clustering approach. Moreover, we present a new score metric to evaluate the performance of the proposed algorithm. We validate our method on 104 days and more than 100k images extracted from 7 users. Results show that behavioural patterns can be discovered to characterize the routine of individuals and consequently their lifestyle.
