Detecting changes between real-world objects using spatiochromatic filters.

Abstract

A behavioral and computational treatment of change detection is reported. The behavioral task was to judge whether a single object substitution change occurred between two "flickering" 9-object scenes. Detection performance was found to vary with the similarity of the changing objects; object changes violating orientation and category yielded the fastest and most accurate detection responses. To account for these data, the BOLAR model was developed, which uses color, orientation, and scale selective filters to compute the visual dissimilarity between the pre- and postchange objects from the behavioral study. Relating the magnitude of the BOLAR difference signals to change detection performance revealed that object pairs estimated as visually least similar were the same object pairs most easily detected by observers. The BOLAR model advances change detection theory by (1) demonstrating that the visual similarity between the change patterns can account for much of the variability in change detection behavior, and (2) providing a computational technique for quantifying these visual similarity relationships for real-world objects.