In recent years, the practice of continuously recording and collecting information about several aspects of individuals’ lives has gained increased popularity. This practice, known as lifelogging, serves multiple purposes, including personal health monitoring and enhancement as well as recording day-to-day activities in hopes of preserving some memories. An essential aspect of this practice lies in the gathering and analysis of image data, offering valuable insights into an individual’s lifestyle, dietary patterns, and physical activities. The NTCIR Lifelog Challenge presents a unique opportunity to delve into the latest advancements in lifelogging research, particularly in the field of image retrieval and analysis. Researchers are encouraged to present their methodologies and participate in lifelog retrieval challenges. Consequently, these challenges allow research teams to assess the efficiency and accuracy of their developed systems using a multimodal dataset derived from an active lifelogger’s 18 months of continuous lifelogging data. This paper presents the current version of MEMORIA, a computational tool that provides an intuitive user interface with several options that allow the user to upload images, explore the segmented events, and perform image retrieval, namely images for the NTCIR Lifelog event. This version of MEMORIA incorporates natural language search capabilities for information retrieval, offering options to filter results based on keywords and time periods. The system integrates image analysis algorithms to process visual lifelogs. These algorithms range from pre-processing algorithms to feature extraction methods, to enrich the annotation of the lifelogs. The paper also includes experimental results of the image annotation methods used in MEMORIA, as well as some examples of user interaction.
Ribeiro et al. (Tue,) studied this question.