基于引导Boosting算法的显著性检测

Saliency detection based on guided Boosting method

针对现有的基于引导学习的显著性检测模型存在的训练样本不纯净和特征提取方式过于简单的问题,提出一种改进的基于引导(Boosting)的算法来检测显著性,从提升训练样本集的准确度和改进特征提取的方式来达到学习效果的提升。首先,根据显著性检测的自底向上模型产生粗选样本图,并通过元胞自动机对粗选样本图进行快速有效优化来建立可靠的引导样本,完成对原图的标注建立训练样本集;然后,在训练集上对样本进行颜色纹理特征提取;最后,使用不同特征不同核的支持向量机(SVM)弱分类器生成基于Boosting学习一个强分类器,对每幅图像的超像素点进行前景背景分类,得到显著图。在ASD数据库和SED1数据库上的实验结果显示该模型能对复杂和简单的图像生成完备清晰的显著图,并在准确率召回率曲线和曲线下面积(AUC)测评值上有较大提升。由于其准确性,能应用在计算机视觉预处理阶段。

Aiming at the problem of impure simplicity and too simple feature extraction of training samples in the existing saliency detection model based on guided learning, an improved algorithm based on Boosting was proposed to detect saliency, which improve the accuracy of the training sample set and improve the way of feature extraction to achieve the improvement of learning effect. Firstly, the coarse sample map was generated from the bottom-up model for saliency detection, and the coarse sample map was quickly and effectively optimized by the cellular automata to establish the reliable Boosting samples. The training samples were set up to mark the original images. Then, the color and texture features were extracted from the training set. Finally, Support Vector Machine （SVM） weak classifiers with different feature and different kernel were used to generate a strong classifier based on Boosting, and the foreground and background of each pixel of the image was classified, and a saliency map was obtained. On the ASD database and the SED1 database, the experimental results show that the proposed algorithm can produce complete clear and salient maps for complex and simple images, with good AUC （Area Under Curve） evaluation value for accuracy-recall curve. Because of its accuracy, the proposed algorithm can be applied in pre-processing stage of computer vision.