We operationalize the concept of familiar configuration by constructing prototypical local shapes, i.e. shapemes, from image data. Shapemes automatically encode mid-level visual cues to figure/ground assignment such as convexity and parallelism. Based on the shapeme representation, we train a logistic classifier to locally predict figure/ground labels. We also consider a global model using a conditional random field (CRF) to enforce global figure/ground consistency at T-junctions. We use loopy belief propagation to perform approximate inference on this model and learn maximum likelihood parameters from ground-truth labels.
We find that the local shapeme model achieves an accuracy of 64% in predicting the correct figural assignment. This compares favorably to previous studies using classical figure/ground cues. We evaluate the global model using either a set of contours extracted from a low-level edge detector or the set of contours given by human segmentations. The global CRF model significantly improves the performance over the local model, most notably when using human-marked boundaries (78%). These promising experimental results show that this is a feasible approach to bottom-up figure/ground assignment in natural images.
Text ReferenceXiaofeng Ren, Charless C. Fowlkes, and Jitendra Malik. Figure/ground assignment in natural images. In ECCV, II: 614–627. 2006.
AUTHOR = "Ren, Xiaofeng and Fowlkes, Charless C. and Malik, Jitendra",
TITLE = "Figure/Ground Assignment in Natural Images",
BOOKTITLE = "ECCV",
YEAR = "2006",
TAG = "grouping,ecological_statistics",
PAGES = "II: 614-627"