目的 图像显著性检测的目的是为了获得高质量的能够反映图像不同区域显著性程度的显著图,利用图像显著图可以快速有效地处理图像中的视觉显著区域。图像的区域协方差分析将图像块的多维特征信息表述为一个协方差矩阵,并用协方差距离来度量两个图像块特征信息的差异大小。结合区域协方差分析,提出一种新的图像显著性检测方法。方法 该方法首先将输入的图像进行超像素分割预处理;然后基于像素块的区域协方差距离计算像素块的显著度;最后对像素块进行上采样用以计算图像像素点的显著度。结果 利用本文显著性检测方法对THUS10000数据集上随机选取的200幅图像进行了显著性检测并与4种不同方法进行了对比,本文方法估计得到的显著性检测结果更接近人工标定效果,尤其是对具有复杂背景的图像以及前背景颜色接近的图像均能达到较好的检测效果。结论 本文方法将图像像素点信息和像素块信息相结合,避免了单个噪声像素点引起图像显著性检测的不准确性,提高了检测精确度;同时,利用协方差矩阵来表示图像特征信息,避免了特征点的数量、顺序、光照等对显著性检测的影响。该方法可以很好地应用到显著目标提取和图像分割应用中。

Objective The purpose of image saliency detection is to obtain high-quality saliency maps that can reflect the significance degrees of different image areas. Based on the saliency map, the visually salient regions of the input images can be processed efficiently, which benefits various applications, such as image segmentation, object detection, and object recognition. Method According to the theoretical analysis of regional covariance, the intrinsic properties of the image superpixels can be described by the high-dimensional covariance matrix, and thus, the dissimilarity degree between two image superpixels can be determined by the regional covariance distance. Using the regional covariance analysis, a novel method for image saliency detection is proposed. First, the input image is preprocessed by superpixel segmentation. Then, the saliency of superpixels can be calculated using the regional covariance distance. Finally, the saliency of superpixels can be up-sampled to determine the saliency of the image pixels. Result In this study, we test 200 images selected from the THUS10000 data set for saliency analysis and compare 4 different detection schemes. Experimental results show that our saliency maps are similar to the ground truth manual calibration results. Our method can effectively estimate the saliency of input images with complex background or with similar color between front and background. Conclusion By combining the high-dimensional intrinsic properties of image pixels and superpixels, our approach can not only avoid the negative effect of single noise pixels but also improve the accuracy of saliency detection. Moreover, by using the covariance matrix of image superpixels, the final saliency map can be robust to the number of feature points, sequence of image pixels, and illumination. The regional-covariance-based image saliency map can be applied to salient object extraction and image segmentation.