The number of people collecting photos has surged owing to social media and cloud services in recent years. A typical approach to summarize a photo collection is dividing it into events and selecting key photos from each event. Despite the fact that a certain event comprises several sub-events, few studies have proposed sub-event segmentation. We propose the sentiment analysis-based photo summarization (SAPS) method, which automatically summarizes personal photo collections by utilizing metadata and visual sentiment features. For this purpose, we first cluster events using metadata of photos and then calculate the novelty scores to determine the sub-event boundaries. Next, we summarize the photo collections using a ranking algorithm that measures sentiment, emotion, and aesthetics. We evaluate the proposed method by applying it to the photo collections of six participants consisting of 5,480 photos in total. We observe that our sub-event segmentation based on sentiment features outperforms the existing baseline methods. Furthermore, the proposed method is also more effective in finding sub-event boundaries and key photos, because it focuses on detailed sentiment features instead of general content features.

近年来，由于社交媒体和云服务，收集照片的人数激增。总结照片集的一种典型方法是将其分为事件，并从每个事件中选择关键照片。尽管特定事件包含多个子事件，但很少有研究提出子事件分割。我们提出了一种基于情感分析的照片汇总（SAPS）方法，该方法通过利用元数据和视觉情感特征自动汇总个人照片集。为此，我们首先使用照片的元数据对事件进行聚类，然后计算新颖性得分以确定子事件边界。接下来，我们使用排名算法总结照片集，该算法可以测量情感，情感和美感。我们通过将其应用于六位参与者的照片集（共5480张照片）来评估所提出的方法。我们观察到，基于情感特征的子事件细分优于现有的基线方法。此外，由于该方法侧重于详细的情感特征而不是一般的内容特征，因此在查找子事件边界和关键照片方面也更加有效。