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We present a nonlocal variational model for saliency detection from still images, from which various features for visual attention can be detected by minimizing the energy functional. The associated Euler-Lagrange equation is a nonlocal

Saliency is an important and basic visual feature for describing image content. It can be particular location, objects, or pixels which stand out relative to their neighbors and thus capture peoples’ attention. The saliency detection technologies, which exploit the most important areas for natural scenes, are very useful in image and video processing such as image retrieval [

Itti et al. [

Different from the biological methods, the pure computational models [

The third category of methods are partly based on biological models and partly on computational ones, that is, the combination of the two ideas. For instance, Harel et al. [

These methods [

In this paper, we focus on the problem of saliency detection in the variational framework. The main advantage of variational methods for image processes is that they can be easily formulated under an energy minimization framework and allow the inclusion of constrains to ensure image regularity while preserving important features. Over the past decades, many researchers have devoted their work to the development of variational models and proposed many good algorithms to solve important topics in image analysis and computer vision, including anisotropic diffusion for image denoising [

Inspired by the nonlocal

The remainder of this paper is organized as follows. In Section

The Ginzburg-Landau equation was originally developed by Ginzburg and Landau [

Recently, nonlocal evolution equations have been widely used to model diffusion processes in many areas [

For the

In this section, we propose a variational model (nonlocal

Let

In the following, we will explain the proposed energy functional defined as (

The functional

The potential

The third term is a fidelity term which forces

In calculus of variations, a standard method to minimize the functional

Equation (

We conclude this subsection by discussing dynamical behavior of the formula (

In this section, we briefly present the numerical algorithm and procedure to solve the evolution equation (

Equation (

In all numerical experiments, we choose the following kernel function:

For color image, let

The proposed nonlocal

Figure

Visual comparison of saliency maps for images under complex background. Column 1: original images. Column 2: the SR model [

In order to perform an objective comparison of the quality of the saliency maps with other methods, we adopt the precision, recall, and F-measure used by Achanta et al. [

Overall mean scores of precision, recall, and F-measure from five different algorithms for 1000 images.

In this paper, we develop a variational model for saliency detection, which bases on the phase transition theory in the fields of mechanics and material sciences. The dynamics of the system, that is, the temporal evolution from the energy functional, yields information of attention. And the process of saliency extraction is interface diffusion. Compared to the existing models for saliency detection, our method provides flexible and intuitive control over the detecting procedure. Experimental results show that the proposed method is effective in extracting important features in terms of human visual perception.

This work was supported by the NSF of China (nos. 61202349 and 61271452), the Natural Science Foundation Project of CQ CSTC (no. cstc2013jcyjA40058), the Education Committee Project Research Foundation of Chongqing (nos. KJ120709 and KJ131209), the Research Foundation of Chongqing University of Arts and Sciences (no. R2012SC20), and the Innovation Foundation of Chongqing (no. KJTD201321).