基于背景先验与低秩恢复的显著性目标检测方法

显著性检测是指计算机通过算法自动识别出图像中的显著性目标,广泛应用于目标识别、图像检索与图像分类等领域。针对现有基于稀疏与低秩矩阵恢复的显著性检测模型中低秩转换矩阵的获取、前景稀疏矩阵的处理以及超像素块之间的关系,需对现有的稀疏与低秩矩阵恢复模型进行优化,使之更好地适用于图像的显著性检测。首先,根据背景的对比度和连通度原则获取图像低秩的背景字典,采用3种尺度分割图像的多个特征矩阵获得图像的前景稀疏矩阵;其次,通过计算邻居像素点之间的影响因子矩阵与置信度矩阵对显著图的结果进行结构约束,并且采用稀疏与低秩矩阵恢复模型对图像进行显著性检测;最后,利用K-means聚类算法的传播机制优化得到的显著图。在公开数据集上进行实验验证,结果证明本文方法能够准确有效地检测出显著性目标。

Salient Object Detection Method Based on Background Prior and Low-rank Matrix Recovery

Saliency object detection means that the computer automatically recognizes the saliency object in the image through the algorithm, which is widely used in many applications such as object recognition, image retrieval and image classification. Aiming at the acquisition of low-rank transformation matrix, the processing of foreground sparse matrix and the relationship between superpixel blocks in the existing significance detection model based on sparse and low-rank matrix restoration, the existing sparse and low-rank matrix restoration model is optimized to make it better applicable to the significance detection of images. Firstly, the low-rank background dictionary is obtained according to the principle of contrast and connectivity. Meanwhile, we used three scales to split multiple feature matrix of images to obtain the foreground sparse matrix of image. Secondly, the structural constraints are made for the results of the significance graph by calculating the influence factor matrix and the confidence matrix between the neighbor pixels, and sparse and low-rank matrix recovery models are used to detect the significance of the image. Finally, the propagation mechanism of K-means clustering algorithm is used to optimize the significant graph. The experimental verification on several datasets shows that the proposed method can accurately and effectively detect saliency object.