A Perceptual Grouping Algorithm Based on Global Salient Structure
Luo Si · Chinese Journal of Computers · 2007
A grouping algorithm based on global salient structure is proposed.Grouping cues are topological properties namely parallelism and closure and local principles namely proximity and continuity.The most salient edge according to probability reference is selected as grouping seed.Edges determined by global statistical dependency are selected as subsequential ones with the most probability of being in the same group with the seed.In perceptual grouping process,attention is employed in grouping to both reduce optimal space and decide pop-out sequence of groups according to their salience.Compared with algorithms adopting local salient relations,above algorithm provides more reliable cues for nature images.This group-based attention makes the effect close to human perception.Experiments on Berkley image database show above algorithm achieves accuracy competitive to Ncut and mini-cut algorithms.It reaches lower error rate and missing rate especially on images with litter texture.Meanwhile,compared with graph cut methods grouping on pixels,the proposed algorithm grouping on edges reduces input dimensionality,therefore less restrictive in image size.