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Web pages with text and a few properly labeled images are relatively easy to make accessible.
The labeled images are usually the most ambiguous ones, using a principle called active learning [3].
These labeled images are treated as the ground truth in the training process.
First, the labeled images are transformed into chroma-like channel C θ i according to (2).
The performance of our proposed method has been quantitatively validated by six indices with respect to manually labeled images.
These features, that we call supervised, are obtained by exploiting the information provided by an additional set of labeled images.
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The source contains labeled image data and the target contains limited labeled image data.
Our method first trains these cascaded networks from labeled image patches.
Further, labeled image data is more scarce as compared to labeled text data.
These algorithms label the binary image first, and then the features are extracted from the labeled image.
Left: original image, middle: labeled image, and right: ground truth mask.
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CEO of Professional Science Editing for Scientists @ prosciediting.com