An energy functional based on variational framework and the phase-transition theory is presented for saliency detection.We describe a Sobolev gradient to find minima of the proposed functional

and then a temporal evolution system for computer visual selection is produced.The process of saliency extraction is a dynamical competition between salient and non-salient component.Comparing the classical

L

2

gradient

we find that the Sobolev metric induces favorable regularity properties in their gradient flow.We demonstrate the consistency between the dynamical behavior of the evolution system and visual selection

which is very important for human to exploit new mechanism of saliency detection.Experimental results on various images show tha

t our model achieves better suppression of the information in background

while achieving higher detection precision of the object

texture

hair

etc

which is important in terms of human visual perception.