采用多層圖模型推理的道路場景分割算法
發(fā)布時間：2018-11-20 21:52
【摘要】：針對傳統(tǒng)圖模型分割算法提取的物體邊緣不夠精細、難以適應復雜道路場景布局的問題,提出了一種基于多層圖模型推理的道路場景分割(HGI)算法。該算法先將圖像過分割為同質的超像素塊,再采用隨機森林模型訓練超像素塊的多類別回歸器和相鄰超像素的一致性回歸器;然后用2種回歸值計算馬爾科夫隨機場(MRF)模型的能量項,通過推理得到初始分割;最后為了解決超像素塊包含多類別帶來的分類混淆,在初始分割基礎上構建像素級的全連接條件隨機場模型,進行優(yōu)化得到精細的分割結果。實驗結果表明,采用HGI算法對人工標注數(shù)據(jù)庫和真實拍攝的場景圖像處理能夠得到精細的分割邊緣,能夠解決超像素推理中的類別混淆問題,與傳統(tǒng)的MRF圖模型分割方法相比,在總體精度和平均召回率2個指標上分別提高了2%和3%。
[Abstract]:Aiming at the problem that the object edge extracted by traditional graph model segmentation algorithm is not fine enough to adapt to the complex road scene layout, a road scene segmentation (HGI) algorithm based on multi-layer graph model reasoning is proposed. The algorithm firstly divides the image into homogeneous super-pixel blocks and then uses a stochastic forest model to train multi-class regression of super-pixel blocks and consistency regression of adjacent super-pixels. Then the energy term of Markov random field (MRF) model is calculated with two regression values, and the initial segmentation is obtained by reasoning. Finally, in order to solve the classification confusion caused by the super-pixel block including multiple categories, a pixel level conditional random field model is constructed on the basis of initial segmentation, and the fine segmentation results are obtained by optimization. The experimental results show that the HGI algorithm can get fine segmentation edge of the artificial tagged database and real scene image processing, and can solve the problem of class confusion in super-pixel reasoning. Compared with the traditional MRF image model segmentation method, the proposed algorithm can solve the problem of classification confusion in super-pixel reasoning. The overall precision and average recall rate were increased by 2% and 3% respectively.
【作者單位】： 西安電子科技大學綜合業(yè)務網理論及關鍵技術國家重點實驗室;
【基金】：國家自然科學基金資助項目(61502364)
【分類號】：TP391.41
[Abstract]:Aiming at the problem that the object edge extracted by traditional graph model segmentation algorithm is not fine enough to adapt to the complex road scene layout, a road scene segmentation (HGI) algorithm based on multi-layer graph model reasoning is proposed. The algorithm firstly divides the image into homogeneous super-pixel blocks and then uses a stochastic forest model to train multi-class regression of super-pixel blocks and consistency regression of adjacent super-pixels. Then the energy term of Markov random field (MRF) model is calculated with two regression values, and the initial segmentation is obtained by reasoning. Finally, in order to solve the classification confusion caused by the super-pixel block including multiple categories, a pixel level conditional random field model is constructed on the basis of initial segmentation, and the fine segmentation results are obtained by optimization. The experimental results show that the HGI algorithm can get fine segmentation edge of the artificial tagged database and real scene image processing, and can solve the problem of class confusion in super-pixel reasoning. Compared with the traditional MRF image model segmentation method, the proposed algorithm can solve the problem of classification confusion in super-pixel reasoning. The overall precision and average recall rate were increased by 2% and 3% respectively.
【作者單位】： 西安電子科技大學綜合業(yè)務網理論及關鍵技術國家重點實驗室;
【基金】：國家自然科學基金資助項目(61502364)
【分類號】：TP391.41
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