An Interactive Watershed-Based Approach for Lifelog Moment Retrieval

Recently, terminologies "lifelogging" and "lifelog" became to represent the activity of continuously recording people everyday experiences and dataset contained these recorded experiences, respectively. Hence, providing an excellent tool to retrieve life moments from lifelogs to fast and accurately bring a memory back to a human when required, become a challenging but exciting task for researchers. In this paper, a new method to meet this challenge by utilizing the hypothesis that a sequence of images taken during a specific period can share the same context and content is introduced. This hypothesis can be explained in another way that if there is one image satisfies a given query (i.e., seed); then a certain number of its spatiotemporal neighbors probably can share the same content and context (i.e., watershed). Hence, an interactive watershed-based approach is applied to build the proposed method that is 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 as well as the advantage of having an interactive schema to push the accuracy when there is a conflict between a query and how to interpret such a query.