BOiS—Berlin Object in Scene Database: Controlled Photographic Images for Visual Search Experiments with Quantified Contextual Priors

Photographic stimuli are often used for studying human perception. To faithfully represent our natural viewing environment, these stimuli should be free of potential artifacts. If stimulus material for scientific experiments is generated from photographs that were created for a different purpose, such as advertisement or art, the scene layout and focal depth might not be typical for our visual world. For instance in advertising photos, particular objects are often centered and focused. In visual search experiments, this can lead to the so-called central viewing bias and an unwanted pre-segmentation of focused objects (Wichmann et al., 2010). Also the photographic process itself can result in artifacts, such as optical, color and geometric distortions, or introduce noise. Furthermore, some image compression methods introduce artifacts that may influence human viewing behavior. In some studies, objects are pasted into scenes using graphics editing. In this case inconsistencies in color, shading or lighting between the object and the local scene background could lead to deviations from natural viewing behavior. In order to meet the needs for publicly available stimulus material in which these artifacts are avoided, we introduce in this paper the BOiS—Berlin Object in Scene database, which provides controlled photographic stimulus material for the assessment of human visual search behavior under natural conditions. The BOiS database comprises high-resolution photographs of 130 cluttered scenes. In each scene, one particular object was chosen as search target. The scene was then photographed three times: with the target object at an expected location, at an unexpected location, or absent. Moreover, the database contains 240 different views of each target object in front of a black background. These images provide different visual cues of the target before the search is initiated. All photos were taken under controlled conditions with respect to photographic parameters and layout and were corrected for optical distortions. The BOiS database allows investigating the top-down influence of scene context, by providing contextual prior maps of each scene that quantify people's expectations to find the target object at a particular location. These maps were obtained by averaging the individual expectations of 10 subjects and can be used to model context effects on the search process. Last not least, the database includes segmentation masks of each target object in the two corresponding scene images, as well as a list of semantic information on the target object, the scene, and the two chosen locations. Moreover, we provide bottom-up saliency measures and contextual prior values at the two target object locations. While originally aimed at visual search, our database can also provide stimuli for experiments on scene viewing and object recognition, or serve as test environment for computer vision algorithms.BMBF, 01GQ0850, Bernstein Fokus Neurotechnologie - Nichtinvasive Neurotechnologie für Mensch-Maschine Interaktio

The developmental trajectory of object recognition robustness: Children are like small adults but unlike big deep neural networks.

In laboratory object recognition tasks based on undistorted photographs, both adult humans and deep neural networks (DNNs) perform close to ceiling. Unlike adults', whose object recognition performance is robust against a wide range of image distortions, DNNs trained on standard ImageNet (1.3M images) perform poorly on distorted images. However, the last 2 years have seen impressive gains in DNN distortion robustness, predominantly achieved through ever-increasing large-scale datasets-orders of magnitude larger than ImageNet. Although this simple brute-force approach is very effective in achieving human-level robustness in DNNs, it raises the question of whether human robustness, too, is simply due to extensive experience with (distorted) visual input during childhood and beyond. Here we investigate this question by comparing the core object recognition performance of 146 children (aged 4-15 years) against adults and against DNNs. We find, first, that already 4- to 6-year-olds show remarkable robustness to image distortions and outperform DNNs trained on ImageNet. Second, we estimated the number of images children had been exposed to during their lifetime. Compared with various DNNs, children's high robustness requires relatively little data. Third, when recognizing objects, children-like adults but unlike DNNs-rely heavily on shape but not on texture cues. Together our results suggest that the remarkable robustness to distortions emerges early in the developmental trajectory of human object recognition and is unlikely the result of a mere accumulation of experience with distorted visual input. Even though current DNNs match human performance regarding robustness, they seem to rely on different and more data-hungry strategies to do so