基于倒数函数谱残差的显著对象探测和提取方法

针对"中心-周围"的显著对象探测方法频繁出现探测或提取对象不完整、边界不平滑以及其9级金字塔下采样的冗余问题，提出一种基于倒数函数-谱残差（RFSR）的显著对象探测方法。首先，利用灰度图像与其对应的高斯低通滤波的差代替"中心-周围"方法中灰度图像标准化，并减少高斯金字塔至6级以降低冗余；其次，利用倒数函数滤波器代替Gabor滤波器提取局部方向信息；接着，利用谱残差方法提取图像的谱特征；最后，将这三个特征经过适当融合生成最终显著图。在两个常用基准数据集上的实验结果表明，所提方法在准确率（precision）、召回率（recall）及F-measure等指标上均比"中心-周围"及谱残差模型有明显提高，其为进一步图像分析、对象识别及基于显著视觉关注的图像检索等理论及应用研究奠定了基础。

Salient object detection and extraction method based on reciprocal function and spectral residual

To solve the problems of "center-surround" salient object detection and extraction method, such as incomplete object detected or extracted, not smooth boundary and redundancy caused by down-sampling 9-level pyramid, a salient object detection method based on Reciprocal Function and Spectral Residual (RFSR) was proposed. Firstly, the difference between the intensity image and its corresponding Gaussian low-pass one was used to substitute the normalization of the intensity image under "center-surround" model, meanwhile the level of Gaussian pyramid was further reduced to 6 to avoid redundancy. Secondly, a reciprocal function filter was used to extract local orientation information instead of Gabor filter. Thirdly, spectral residual algorithm was used to extract spectral feature. Finally, three extracted features were properly combined to generate the final saliency map. The experimental results on two mostly common benchmark datasets show that compared with "center-surround" and spectral residual models, the proposed method significantly improves the precision, recall and F-measure, furthermore lays a foundation for subsequent image analysis, object recognition, visual-attention-based image retrieval and so on.