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Scribble-based object segmentation with modified gaussian mixture models
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DOI：10.1007/s10044-014-0406-6.png)
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摘要
En 中文
In this paper, we present an interactive segmentation method, designed to help the user to extract an object of interest from an image. The proposed approach adopts the scribble-based segmentation paradigm. The user interaction consists of specifying a set of lines, corresponding to both foreground and background scribbles. The segmentation process is based on color distributions, estimated with Gaussian mixture models (GMM). We show that such a technique presents some limitations when dealing with compressed images, even for relatively high quality compression factors: in this case, blocking artifacts may degrade the segmentation results. In order to overcome such a drawback, a modified GMM model, which re-shapes the Gaussian mixture based on the eigenvalues of the GMM components, is proposed. The experimental evaluation, carried out on a corpus of various images with different characteristics and textures, demonstrates the superiority of the modified GMM model which is able to appropriately take into account compression artifacts.
Keyword:
Scribble-based interactive image segmentation
Foreground extraction
Gaussian mixture model
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