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Higher-Order Image Co-segmentation
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DOI：10.1109/TMM.2016.2545409.png)
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Abstract
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A novel interactive image cosegmentation algorithm using likelihood estimation and higher order energy optimization is proposed for extracting common foreground objects from a group of related images. Our approach introduces the higher order clique's, energy into the cosegmentation optimization process successfully. Aregion-based likelihood estimation procedure is first performed to provide the prior knowledge for our higher order energy function. Then, a new cosegmentation energy function using higher order cliques is developed, which can efficiently cosegment the foreground objects with large appearance variations from a group of images in complex scenes. Both the quantitative and qualitative experimental results on representative datasets demonstrate that the accuracy of our cosegmentation results is much higher than the state-of-the-art cosegmentation methods.
Keywords:
Energy optimization
higher order cliques
image cosegmentation
likelihood estimation
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