English
资源说明：We propose an adaptive figure-ground classification
algorithm to automatically extract a foreground region
using a user-provided bounding-box. The image is first
over-segmented with an adaptive mean-shift algorithm,
from which background and foreground priors are estimated.
The remaining patches are iteratively assigned based on their distances to the priors, with the foreground
prior being updated online. A large set of candidate segmentations are obtained by changing the initial foreground prior. The best candidate is determined by a score function that evaluates the segmentation quality. Rather than using a single distance function or score function, we generate multiple hypothesis segmentations from different combinations of distance measures and score functions. The final segmentation is then automatically obtained with a voting or weighted combination scheme from the multiple hypotheses. Experiments indicate that our method performs at or above the current state-of-the-art on several datasets,with particular success on challenging scenes that contain irregular or multiple-connected foregrounds. In addition, this improvement in accuracy is achieved with low computational cost.
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