Disentangling the Roles of Junctions and Spatial Relations Between Contours for Scene Categorization

Humans can rapidly and accurately determine the class of a natural scene. With severely limited exposure time (~50ms stimulus duration) it is unlikely that the visual system recognizes the individual objects. Instead, efficiently obtained summary statistics might be used to classify the scene. Walther et al (2011) found that observers can rapidly classify line-drawings of natural scenes. Walther and Shen (2014) showed that line-drawing scenes with randomly translated contours result in different confusions than with intact line drawings, suggesting that relationships between lines (e.g. junctions) are important for scene classification. In the current study, we showed subjects intact line-drawings of natural scenes or manipulated line-drawings from the same database. Manipulated scenes either contained a portion of the line segments at junctions, or they contained only line segments between (not including) the junctions. The total amount of line content was equal in both manipulations. Subjects made similar confusions in all conditions (intact, junction, non-junction). This suggests that junctions (and possibly their relationships) can be used to perform scene classification, in the absence of their connecting lines. Subject performance (percent correct) was better in the non-junction images than in the junction images, suggesting that non-junction relationships between lines (e.g. parallelism) are powerful cues to scene category, and this information can be rapidly extracted for use in scene classification. It is unclear if the observers extrapolate line segments in order to infer junctions (in non-junction images), or interpolate a line between junctions (in junction images), which should be controlled in future experiments