自然场景下多区域特征融合的混合航拍图像分割算法

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...

Hybrid aerial image segmentation algorithm based on multi-region feature fusion for natural scene

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.