Currently, the most prominent object recognition and image labeling techniques are based on the region proposal algorithms. One of the significant challenges of the region proposal algorithms is to achieve high Recall at high overlaps. This paper proposes a new region proposal algorithm using perceptual grouping to generate fitting regions to enhance the Recall at high overlaps. The proposed method comprises segmentation, region merging, based on texture descriptors, and similarity measurement. Furthermore, the algorithm introduces a hybrid approach to compute an efficient threshold. To fully assess the proposed algorithm, well-known metrics such as overlap and Recall are measured. Experimental results are reported on MSRC, VOC2007, VOC2012, and COCO 2017 datasets. The results are compared with segmentation algorithms, and several classical and deep learning-based region proposals. The evaluation results indicate a good improvement of the Recall at high overlaps, such as 0.8 and 0.9, with a reasonable number of regions.

当前，最突出的对象识别和图像标记技术是基于区域提议算法的。区域提议算法的重大挑战之一是在高重叠度下实现高召回率。本文提出了一种新的区域提议算法，该算法使用感知分组来生成拟合区域以增强高重叠区域的召回率。所提出的方法包括分割，基于纹理描述符的区域合并以及相似性测量。此外，该算法引入了一种混合方法来计算有效阈值。为了全面评估提出的算法，需要对诸如重叠和召回率之类的众所周知的指标进行测量。在MSRC，VOC2007，VOC2012和COCO 2017数据集上报告了实验结果。将结果与细分算法进行比较，以及一些基于经典和深度学习的区域提案。评估结果表明，在具有合理数量的区域的高重叠度（例如0.8和0.9）下，召回率得到了很好的改善。