基于滤波合成的关键显著性目标检测方法

针对显著性目标检测过程中的背景干扰问题,提出了一种基于滤波合成的关键显著性目标检测算法。该算法将局部指导滤波与改进的差分高斯(DoG）滤波方法相结合,使显著性目标更加凸显；然后,利用得到的显著性图确定关键点集合,通过调整因子得到更符合视觉机制的显著性检测结果。实验表明,所提算法优于现有显著性检测方法。与局部对比度(LC)方法、谱残差(SR)方法、基于直方图对比度(HC)方法、区域对比度(RC)方法、基于调频(FT)的方法等相比,背景与干扰目标得到有效抑制,同时具有更高的精度和更好的召回率。

Key salient object detection based on filtering integration method

Concerning the problem of the background interference during the salient object detection, a key salient object detection algorithm was proposed based on filtering integration in this paper. The proposed algorithm integrated the locally guided filtering with the improved DoG (Difference of Gaussia) filtering, and made the salient object more highlighted. Then, the key points set was determined by using the saliency map, and the result of saliency detection was got by adjustment factor, which was more suitable for human visual system. The experimental results show that the proposed algorithm is superior to existing significant detection methods. And it can restrain the background interference effectively, and have higher precision and better recall rate compared with other methods, such as Local Contrast (LC), Spectral Residual (SR), Histogram-based Contrast (HC), Region Contrast (RC) and Frequency-Tuned (FT).