Perceptual popout is defined by both feature similarity and local feature contrast. We identify these two measures with attraction and repulsion, and unify the dual processes of association by attraction and segregation by repulsion in a single grouping framework. We generalize normalized cuts to multi-way partitioning with these dual measures. We expand graph partitioning approaches to weight matrices with negative entries, and provide a theoretical basis for solution regularization in such algorithms. We show that attraction, repulsion and regularization each contributes in a unique way to popout. Their roles are demonstrated in various salience detection and visual search scenarios. This work opens up the possibilities of encoding negative correlations in constraint satisfaction problems, where solutions by simple and robust eigendecomposition become possible.