Abstract: In the two components of hybrid image segmentation algorithm, the initial segmentation cannot form the over-segmentation region sets with low wrong segmentation rate, while region merging lacks the label selection mechanism for region merging and the method of determining region merging stopping moment in this component commonly does not meet the scenario requirements. To solve the above problems, a Multi-level Region Information fusion based Hybrid image Segmentation algorithm (MRIHS) was proposed. Firstly, the improved Markov model was used to smooth the superpixel blocks, so as to form initial segmentation regions. Then, the designed region label selection mechanism was used to select the labels of the merged regions after measuring the similarity of the initial segmentation regions and selecting the region pairs to be merged. Finally, an optimal merging state was defined to determine region merging stopping moment. To verify MRIHS performance, comparison experiments between this algorithm with Multi-dimensional Feature fusion based Hybrid image Segmentation algorithm (MFHS), Improved FCM image segmentation algorithm based on Region Merging (IFRM), Inter-segment and Boundary Homogeneities based Hybrid image Segmentation algorithm (IBHHS), Multi-dimensional Color transform and Consensus based Hybrid image Segmentation algorithm (MCCHS) were carried out on Visual Object Classes (VOC), Cambridge-driving labeled Video database (CamVid) and the self-built river and lake inspection (rli) datasets. The results show that on VOC and rli datasets, the Boundary Recall (BR), Achievable Segmentation Accuracy (ASA), recall and dice of MRIHS are at least increased by 0.43 percentage points, 0.35 percentage points, 0.41 percentage points, 0.84 percentage points respectively and the Under-segmentation Error (UE) of MRIHS is at least decreased by 0.65 percentage points compared with those of other algorithms; on CamVid dataset, the recall and dice of MRIHS are at least improved by 1.11 percentage points, 2.48 percentage points respectively compared with those of other algorithms.

Key words: initial segmentation, region merging, hybrid image segmentation algorithm, Block Markov Random Field (BMRF), superpixel block

摘要： 混合图像分割算法所包含的两个部件中，初始分割不能形成低误分割率的过分割区域集，而区域合并存在缺少区域合并标号选择机制，且存在确定区域合并停止时刻的方式常不满足场景需求的不足。针对以上问题，提出一种基于多级区域信息融合的混合图像分割算法（MRIHS）。首先，使用改进的马尔可夫模型平滑超像素块，以形成初始分割区域；其次，在对初始分割区域进行相似性度量并选定待合并区域对后，利用设计出的区域标号选择机制来选定合并后的区域标号；最后，定义一种最佳合并状态以确定合并停止时刻。为验证MRIHS性能，在视觉对象类别（VOC）、剑桥驾驶标签视频数据库（CamVid）、自建的河湖巡检（rli）数据集上，将其与基于多维特征融合的混合图像分割算法（MFHS）、改进的基于区域合并的FCM图像分割算法（IFRM）、基于段间和边界均质性的混合图像分割算法（IBHHS）、基于多维色彩变换与一致性的混合图像分割算法（MCCHS）进行对比。结果表明：MRIHS在VOC、rli数据集上的边缘召回率（BR）、可达分割精准度（ASA）、查全率、重合率至少分别比其余算法提高了0.43个百分点、0.35个百分点、0.41个百分点、0.84个百分点；欠分割误差（UE）至少减少了0.65个百分点。在CamVid数据集上，MRIHS的查全率、重合率指标至少比其余算法提高了1.11个百分点、2.48个百分点。

关键词: 初始分割, 区域合并, 混合图像分割, 块状马尔可夫随机场, 超像素块