基于图割精细化和可微分聚类的无监督显著性目标检测

Unsupervised salient object detection based on graph cut refinement and differentiable clustering

针对传统显著性检测算法分割精度低以及基于深度学习的显著性检测算法对像素级人工注释数据依赖性过强等不足,提出一种基于图割精细化和可微分聚类的无监督显著性目标检测算法。该算法采用由“粗”到“精”的思想,仅利用单张图像的特征便可以实现精确的显著性目标检测。首先利用Frequency-tuned算法根据图像自身的颜色和亮度得到显著粗图,然后根据图像的统计特性进行二值化并结合中心优先假设得到显著目标的候选区域,进而利用基于单图像进行图割的GrabCut算法对显著目标进行精细化分割,最后为克服背景与目标极为相似时检测不精确的困难,引入具有良好边界分割效果的无监督可微分聚类算法对单张显著图做进一步的优化。所提出的算法在ECSSD和SOD数据集上进行测试并与现有的7种算法进行对比,结果表明得到的优化显著图更接近于真值图,在ECSSD和SOD数据集上分别实现了14.3%和23.4%的平均绝对误差(MAE)。

Concerning that the traditional saliency detection algorithm has low segmentation accuracy and the deep learning-based saliency detection algorithm has strong dependence on pixel-level manual annotation data,an unsupervised saliency object detection algorithm based on graph cut refinement and differentiable clustering was proposed.In the algorithm,the idea of“coarse”to“fine”was adopted to achieve accurate salient object detection by only using the characteristics of a single image.Firstly,Frequency-tuned algorithm was used to obtain the salient coarse image according to the color and brightness of the image itself.Then,the candidate regions of the salient object were obtained by binarization according to the image’s statistical characteristics and combination of the central priority hypothesis.After that,GrabCut algorithm based on single image for graph cut was used for segmenting the salient object finely.Finally,in order to overcome the difficulty of imprecise detection when the background was very similar to the object,the unsupervised differentiable clustering algorithm with good boundary segmentation effect was introduced to further optimize the saliency map.Experimental results show that compared with the existing seven algorithms,the optimized saliency map obtained by the proposed algorithm is closer to the ground truth,achieving an Mean Absolute Error(MAE)of 14.3%and 23.4%on ECSSD and SOD datasets,respectively.