多尺度的图像显著性检测方法

A Multiscale Image Saliency Detection Method

摘要:

为了提高显著性检测算法的准确性与鲁棒性，提出了一种基于多尺度融合的对象显著性检测方法.首先对图像进行平滑处理，过滤掉图像中的高频噪声特征，然后对图像进行尺度划分并分别采用不同的方法对不同尺度上的图像检测其显著性，最后根据条件随机场理论对不同尺度上的显著性检测结果进行加权融合，得到最终的显著性检测结果.在两种公共数据集上与多种经典算法进行定性、量化比较，结果表明该算法具有更好的表现.

Abstract:

In order to improve the accuracy and robustness of the saliency detection algorithm, this paper proposed a multiscale image saliency detection method. First, the smoothing algorithm was adopted to filter out the noise characteristics in the image. Then, the multiscale representation of an image was performed and saliency maps were computed at different scales. Finally, according to the conditional random field theory, the saliency detection results at different scales were weighted together to get the final results. Extensive experiments in which the proposed method was compared with 9 existing stateoftheart methods on five benchmark data sets, ECSSD and MSRA10K, show that the proposed method performs better in terms of various evaluation metrics.