Categorization performance is a popular metric of scene recognition and understanding in behavioral and computational research. However, categorical constructs and their labels can be somewhat arbitrary. Derived from exhaustive vocabularies of place names (e.g., Deng et al., 2009 ), or the judgements of small groups of researchers (e.g., Fei-Fei, Iyer, Koch, & Perona, 2007 ), these categories may not correspond with human-preferred taxonomies. Here, we propose clustering by increasing the rand index via coordinate ascent (CIRCA): an unsupervised, data-driven clustering method for deriving ground-truth scene categories. In Experiment 1 , human participants organized 80 stereoscopic images of outdoor scenes from the Southampton-York Natural Scenes (SYNS) dataset ( Adams et al., 2016 ) into discrete categories. In separate tasks, images were grouped according to i) semantic content, ii) three-dimensional spatial structure, or iii) two-dimensional image appearance. Participants provided text labels for each group. Using the CIRCA method, we determined the most representative category structure and then derived category labels for each task/dimension. In Experiment 2 , we found that these categories generalized well to a larger set of SYNS images, and new observers. In Experiment 3 , we tested the relationship between our category systems and the spatial envelope model ( Oliva & Torralba, 2001 ). Finally, in Experiment 4 , we validated CIRCA on a larger, independent dataset of same-different category judgements. The derived category systems outperformed the SUN taxonomy ( Xiao, Hays, Ehinger, Oliva, & Torralba, 2010 ) and an alternative clustering method ( Greene, 2019 ). In summary, we believe this novel categorization method can be applied to a wide range of datasets to derive optimal categorical groupings and labels from psychophysical judgements of stimulus similarity.

Many species employ camouflage to disguise their true shape and avoid detection or recognition. Disruptive coloration is a form of camouflage in which high-contrast patterns obscure internal features or break up an animal's outline. In particular, edge enhancement creates illusory, or ‘fake’ depth edges within the animal's body. Disruptive coloration often co-occurs with background matching, and together, these strategies make it difficult for an observer to visually segment an animal from its background. However, stereoscopic vision could provide a critical advantage in the arms race between perception and camouflage: the depth information provided by binocular disparities reveals the true three-dimensional layout of a scene, and might, therefore, help an observer to overcome the effects of disruptive coloration. Human observers located snake targets embedded in leafy backgrounds. We analysed performance (response time) as a function of edge enhancement, illumination conditions and the availability of binocular depth cues. We confirm that edge enhancement contributes to effective camouflage: observers were slower to find snakes whose patterning contains ‘fake’ depth edges. Importantly, however, this effect disappeared when binocular depth cues were available. Illumination also affected detection: under directional illumination, where both the leaves and snake produced strong cast shadows, snake targets were localized more quickly than in scenes rendered under ambient illumination. In summary, we show that illusory depth edges, created via disruptive coloration, help to conceal targets from human observers. However, cast shadows and binocular depth information improve detection by providing information about the true three-dimensional structure of a scene. Importantly, the strong interaction between disparity and edge enhancement suggests that stereoscopic vision has a critical role in breaking camouflage, enabling the observer to overcome the disruptive effects of edge enhancement.

No abstract

No abstract

No abstract