imageCLEFlifelog2019 introduces two exciting challenges of getting insights from lifelogs. Both challenges aim to have a memory assistant that can accurately bring a memory back to a human when necessary. In this paper, two new methods to tackle these challenges by leveraging the content and context of daily activities are introduced. Two backbone hypotheses are built based on observations:(1) under the same context (eg, a particular activity), one image should have at least one associative image (eg, same content, same concepts) taken from different moments. Thus, a given set of images can be rearranged chronologically by ordering their associative images whose orders are known precisely, and (2) a sequence of images taken during a specific period can share the same context and content. Thus, if a set of images can be clustered into sequential atomic clusters, given an image, it is possible to automatically find all images sharing the same content and context by first finding the atomic cluster sharing the same content, then watershed reward and forward to find other clusters sharing the same context. The proposed methods are evaluated on the imageCLEFlifelog 2019 dataset and compared to participants joined this event. The experimental results confirm the high productivity of the proposed method in both stable and accuracy aspects.