Color contributes to object-contour perception in natural scenes.

The magnitudes of chromatic and achromatic edge contrast are statistically independent and thus provide independent information, which can be used for object-contour perception. However, it is unclear if and how much object-contour perception benefits from chromatic edge contrast. To address this question, we investigated how well human-marked object contours can be predicted from achromatic and chromatic edge contrast. We used four data sets of human-marked object contours with a total of 824 images. We converted the images to the Derrington-Krauskopf-Lennie color space to separate chromatic from achromatic information in a physiologically meaningful way. Edges were detected in the three dimensions of the color space (one achromatic and two chromatic) and compared to human-marked object contours using receiver operating-characteristic (ROC) analysis for a threshold-independent evaluation. Performance was quantified by the difference of the area under the ROC curves (ΔAUC). Results were consistent across different data sets and edge-detection methods. If chromatic edges were used in addition to achromatic edges, predictions were better for 83% of the images, with a prediction advantage of 3.5% ΔAUC, averaged across all data sets and edge detectors. For some images the prediction advantage was considerably higher, up to 52% ΔAUC. Interestingly, if achromatic edges were used in addition to chromatic edges, the average prediction advantage was smaller (2.4% ΔAUC). We interpret our results such that chromatic information is important for object-contour perception.