On the Hunt: Searching for Poorly Defined Camouflaged Targets

The Oddity Detection in Diverse Scenes (ODDS) database: Validated real-world scenes for studying anomaly detection

Many applied screening tasks (e.g., medical image or baggage screening) involve challenging searches for which standard laboratory search is rarely equivalent. For example, whereas laboratory search frequently requires observers to look for precisely defined targets among isolated, non-overlapping images randomly arrayed on clean backgrounds, medical images present unspecified targets in noisy, yet spatially regular scenes. Those unspecified targets are typically oddities, elements that do not belong. To develop a closer laboratory analogue to this, we created a database of scenes containing subtle, ill-specified "oddity" targets. These scenes have similar perceptual densities and spatial regularities to those found in expert search tasks, and each includes 16 variants of the unedited scene wherein an oddity (a subtle deformation of the scene) is hidden. In Experiment 1, eight volunteers searched thousands of scene variants for an oddity. Regardless of their search accuracy, they were then shown the highlighted anomaly and rated its subtlety. Subtlety ratings reliably predicted search performance (accuracy and response times) and did so better than image statistics. In Experiment 2, we conducted a conceptual replication in which a larger group of naïve searchers scanned subsets of the scene variants. Prior subtlety ratings reliably predicted search outcomes. Whereas medical image targets are difficult for naïve searchers to detect, our database contains thousands of interior and exterior scenes that vary in difficulty, but are nevertheless searchable by novices. In this way, the stimuli will be useful for studying visual search as it typically occurs in expert domains: Ill-specified search for anomalies in noisy displays.

Training detection of camouflaged targets in natural scenes: Backgrounds and targets both matter

As target-background similarity increases, search performance declines, but this pattern can be attenuated with training. In the present study we (1) characterized training and transfer effects in visual search for camouflaged targets in naturalistic scenes, (2) evaluated whether transfer effects are preserved 3 months after training, (3) tested the suitability of the perceptual learning hypothesis (i.e., using learned scene statistics to aid camouflaged target detection) for explaining camouflage search improvements over training, and (4) provide guidance for camouflage detection training in practice. Participants were assigned to one of three training groups: adaptive camouflage (difficulty varied by performance), massed camouflage (difficulty increased over time), or an active control (no camouflage), and trained over 14 sessions. Additional sessions measured transfer (immediately post training) and retention of training benefits (10 days and 3 months post training). Both the adaptive and massed training groups showed improved camouflaged target detection up to 3 months following training, relative to the control. These benefits were observed only with backgrounds and targets that were similar to those experienced during training and are broadly consistent with the perceptual learning hypothesis. In practice, training interventions should utilize stimuli similar to the operational environment in which detection is expected to occur.

Expert camouflage-breakers can accurately localize search targets

Camouflage-breaking is a special case of visual search where an object of interest, or target, can be hard to distinguish from the background even when in plain view. We have previously shown that naive, non-professional subjects can be trained using a deep learning paradigm to accurately perform a camouflage-breaking task in which they report whether or not a given camouflage scene contains a target. But it remains unclear whether such expert subjects can actually detect the target in this task, or just vaguely sense that the two classes of images are somehow different, without being able to find the target per se. Here, we show that when subjects break camouflage, they can also localize the camouflaged target accurately, even though they had received no specific training in localizing the target. The localization was significantly accurate when the subjects viewed the scene as briefly as 50 ms, but more so when the subjects were able to freely view the scenes. The accuracy and precision of target localization by expert subjects in the camouflage-breaking task were statistically indistinguishable from the accuracy and precision of target localization by naive subjects during a conventional visual search where the target ‘pops out’, i.e., is readily visible to the untrained eye. Together, these results indicate that when expert camouflage-breakers detect a camouflaged target, they can also localize it accurately.