改进凸包的贝叶斯模型显著性检测算法

针对传统贝叶斯模型算法对图像显著区域检测精度需要进一步提高的问题,提出一种改进凸包的贝叶斯模型显著性检测算法。首先,利用流行排序算法对图像进行前景提取,提取的前景区域作为贝叶斯模型的先验概率;其次,利用颜色增强的Harris角点检测算法检测图像在RGB,HSV,CIELab 3个颜色空间中的特征点,分别构造RGB,HSV,CIELab空间的凸包,求取3个颜色空间下的凸包的交集;再次,通过贝叶斯模型根据先验概率、凸包与颜色直方图结合得到的观测似然概率计算获得显著性区域图;最后,将新算法在两大公开数据集MSRA和ECSSD中进行测试。结果表明,新算法能够有效抑制背景噪声,完整检出显著区域,F-measure值在MSRA和ECSSD数据库中的测试结果分别为0.87和0.71,准确率-召回率曲线在复杂图像数据库高于传统经典算法。新算法改进了传统经典算法的检测效果,进一步提高了显著图检测的准确性。

Bayesian model saliency detection algorithm based on improved convex hull

Aiming at the problem of poor precision performance of traditional Bayesian model saliency detection algorithm, a Bayesian model saliency detection algorithm based on improved convex hull was proposed. Firstly, the foreground of the image was extracted by the manifold ranking algorithm, which was used as the prior probability in Bayesian model. Secondly, Harris corner detection algorithm based on color enhancement was used to detect the feature points of the image in three color spaces of RGB, HSV and CIELab; the convex hulls in RGB, HSV and CIELab spaces were constructed respectively; and the intersection of convex hulls were obtained. Thirdly, the saliency region map was calculated by Bayesian model according to the prior probability and the observed likelihood probability obtained by combining convex hulls and color histograms. Finally, the proposed algorithm was tested in two public data sets MSRA and ECSSD. The experimental results show that the proposed algorithm can suppress the background noise effectively and detect the salient areas completely. The test results of F-measure value in MSRA and ECSSD databases are 0.87 and 0.71 respectively, and the accuracy-recall rate curve is higher than that of traditional classical algorithms in complex image databases. The proposed algorithm improves the detection effect of the traditional classical algorithm and the accuracy of saliency map detection.