含平滑处理的图像分割算法比较

Comparison for image segmentation algorithms with smoothing processing

图像理解的基础是图像语义分割。通过引入块形状复杂度并结合欧拉数,构建了过分割块合并的图模型图像分割算法(GM)。按照先图像平滑再图像分割的思路,把双向滤波、保边平滑、总变差结构提取平滑和滚动引导滤波等4种平滑算法与阈值分割、主动轮廓、粒子群优化算法和GM等4种分割算法相组合,形成16套先平滑再分割的两阶段图像分割算法。通过实验进行分割比较,并根据校正兰德指数(CRI)和杰卡德指数(JI)进行量化分析。实验结果表明:总变差结构提取平滑与GM相结合取得的分割结果最佳。t检验显示,平滑预处理能够显著提高GM算法的分割效果。

Image understanding is based on image semantic segmentation. By employing patch shape complexity index and Euler number,an image segmentation algorithm（ GM） based on oversegmentation,patch merging and graph model is presented. Following a common technical tactic that an image is segmented after smoothing,16 algorithms for image segmentation are presented by combining each of the 4 filter methods including Bilateral Filtering（ Bi F）, Edge Preserving Smoothing（ EPS）,structure extraction via Relative Total Variation（ RTV） and Rolling Guidance Filter（ RGF）, with each of the 4 segmentation algorithms including Threshold Segmentation（ Thr S）,Active Contours without Edges（ ACn E）,Particle Swarm Optimization（ PSO）,and GM.To evaluate the efficiency of the algorithms,the comparative experiments are conducted,and two evaluation indexes, Corrected Rand Index（ CRI） and Jaccard Index（ JI）, are used in the experiments. The experimental results show that the algorithm which segments an image with GMafter RTV has the best performance from CRI and JI. T-test also shows that it is statistically significant that the segmentation effect of GM algorithm is improved by using image smoothing.