A user study on localized sub-region search in homogeneous marine collections

Searching in highly homogeneous video domains is challenging, especially when relying solely on human memory. This difficulty arises because homogeneous content often requires domain-specific vocabulary to describe effectively, and a single text label might fit a substantial subset of the database. This paper investigates supplementing text queries with spatial location information to better address specific search intents—a strategy applicable when users possess strong visual memory of a target object, or have external knowledge of its position. To process this location information effectively, we formally define and evaluate static grid and dynamic segmentation strategies for video frame partitioning. Furthermore, we present a large-scale cognitive user study involving 220 participants, designed to simulate realistic memory constraints of Known-Item Search (KIS) tasks commonly found in interactive retrieval benchmarks. The study utilizes standard working memory interference techniques, comprising a target exposure phase, a distraction period, and subsequent query specification from memory. These queries are then evaluated against the proposed spatial retrieval models to determine how human spatial memory decay impacts retrieval effectiveness. Our results reveal that while dynamic segmentation models achieve the highest theoretical retrieval bounds under perfect conditions, their strict geometric boundaries are severely affected by human memory decay. Consequently, static overlapping grids demonstrate high robustness, outperforming dynamic models under more realistic, memory-driven search constraints. Finally, our experiments show that the observed spatial annotation perturbations can be modeled using a four-dimensional Kernel Density Estimation (KDE) method, enabling the simulation of realistic human memory decay on ideal bounding boxes.