Challenges of Personal Image Retrieval and Organization: An Academic Perspective

With the increasing number of smartphone devices and social media platforms, many users now have large personal image collections. As these collections expand, the task of organizing and retrieving specific images becomes increasingly challenging. To quantify the scope of this emerging problem, we conducted a quantitative survey study to gain insights into users’ practices concerning personal image retrieval and organization. Initially, we conducted a survey on the Florida State University campus, primarily targeting undergraduate and graduate students. The survey questionnaire delved into various aspects of how users organize and retrieve their images. Then we implemented several machine learning models (decision trees, random forest, logistic regression, and XGboost) on the collected survey data to determine the existence of problems faced by the users to retrieve their personal images. XGboost performed better than the other models with an accuracy of 73%. The model also revealed that several factors, such as the frequency of encountering difficulties in finding photos both before and after sharing, the number of photos taken by users in the previous year, the number of photos posted on social media, and the total number of photos stored on users’ laptops or desktops, were among the most critical features associated with personal image retrieval challenges.