基于对比度优化流形排序的显著目标检测算法

现有的基于背景先验的显著性算法模型中存在先验区域选取不合理的问题，导致计算出的前景区域不准确，影响最终结果。针对该问题提出了基于对比度优化流形排序的显著目标检测算法。利用图像边界信息找出背景先验，设计出采用显著期望、局部对比度以及全局对比度三个指标来衡量先验质量的算法，并根据先验质量设计带权加法，代替简单乘法融合显著先验，从而使显著先验更加准确。从先验中提取显著区域时，更改了选取阈值的策略，更合理地选取出前景区域，再利用流形排序得到显著性图，从而使显著性检测结果更加准确。实验结果表明，与同类算法相比，所提算法突出显著区域，减少噪声，更符合人类视觉感知，并在处理时间上领先于深度学习方法。

Salient target detection algorithm based on contrast optimized manifold ranking

The existing boundary prior based saliency algorithm model has the problem of improper selection of reasonable saliency prior region, which leads to the inaccurate foreground region and influence the final result. Aiming at this problem, a salient target detection algorithm based on contrast optimized manifold ranking was proposed. The image boundary information was utilized to find the background prior. An algorithm for measuring the priori quality was designed by using three indexes, namely, saliency expection, local contrast and global contrast. A priori quality design with weighted addition replaced simple multiplication fusion to make the saliency prior more accurate. When the salient regions were extracted from the a priori, the strategy of selecting the threshold was changed, the foreground region was selected more rationally, and the saliency map was obtained by using the manifold ranking, so that the saliency detection result was more accurate. The experimental results show that the proposed algorithm outperforms the similar algorithms, reduces the noise, which is more suitable for human visual perception, and ahead of the depth learning method in processing time.