Learning a Classification Model for Segmentation

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Learning a Classification Model for Segmentation
We propose a two-class classification model for grouping. Human segmented natural images are used as positive examples. Negative examples of grouping are constructed by randomly matching human segmentations and images. In a preprocessing stage an image is oversegmented into superpixels. We define a variety of features derived from the classical Gestalt cues, including contour, texture, brightness and good continuation. Information-theoretic analysis is applied to evaluate the power of these grouping cues. We train a linear classifier to combine these features. To demonstrate the power of the classification model, a simple algorithm is used to randomly search for good segmentations. Results are shown on a wide range of images.
Xiaofeng Ren, Jitendra Malik
Added 15 Oct 2009
Updated 15 Oct 2009
Type Conference
Year 2003
Where ICCV
Authors Xiaofeng Ren, Jitendra Malik
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