全局显著结构主导下的知觉编组算法

A Perceptual Grouping Algorithm Based on Global Salient Structure

提出一种建立在可靠的全局线索基础上的编组算法.编组线索为反映全局显著结构的拓扑特征闭合性和平行性以及局部规律邻接性和连续性.依据概率推理选择最显著的边缘作为种子,依据全局依赖性选择最有可能与种子属于同一编组的边缘.编组的形成中融入注意机制,一方面缩小寻优空间另一方面确定各编组被检测的顺序.在Berkley图像库上的实验表明,该算法至少具有与Ncut和mini-cut相当的准确率,特别对纹理少的图像能够有效地降低错编率与漏编率.同时由于对边缘进行编组降低了输入数据的维数,因此比Ncut和mini-cut更少地受到图像尺寸的限制.

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.