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دسته بندی تطبیقی ترکیب زمینه
سال انتشار: 2012 - تعداد صفحات فایل pdf انگلیسی: 8 - تعداد صفحات فایل doc فارسی: 27 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.
Keywords: Delta modulation | Bandwidth | Image segmentation | Classification algorithms | Color | Gaussian distribution | Covariance matrix |
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