宁波大学学报（理工版）
寧波大學學報（理工版）
저파대학학보（리공판）
JOURNAL OF NINGBO UNIVERSITY(NSEE)
2014年
1期
23-28
,共6页
图像分割%变分推断%高斯混合模型%期望最大化算法
圖像分割%變分推斷%高斯混閤模型%期望最大化算法
도상분할%변분추단%고사혼합모형%기망최대화산법
image segmentation%variational inference%Gaussian mixture models%expectation-maximization
提出了一种基于变分推断的高斯混合模型的图像分割算法。该算法首先用贝叶斯混合高斯模型对图像的特征进行建模，并针对模型的参数学习问题，利用变分推断算法估计模型的参数及其后验概率；这种方法比采样法的计算量更少，而且能够根据图像数据自动优化混合个数，实现了模型的自动选择。最后，该算法在 Berkeley 的自然图像集上进行的实验结果与经典的图像分割算法进行了比较，结果表明此方法得到的图像分割结果精度较高，具有较好的性能。
提齣瞭一種基于變分推斷的高斯混閤模型的圖像分割算法。該算法首先用貝葉斯混閤高斯模型對圖像的特徵進行建模，併針對模型的參數學習問題，利用變分推斷算法估計模型的參數及其後驗概率；這種方法比採樣法的計算量更少，而且能夠根據圖像數據自動優化混閤箇數，實現瞭模型的自動選擇。最後，該算法在 Berkeley 的自然圖像集上進行的實驗結果與經典的圖像分割算法進行瞭比較，結果錶明此方法得到的圖像分割結果精度較高，具有較好的性能。
제출료일충기우변분추단적고사혼합모형적도상분할산법。해산법수선용패협사혼합고사모형대도상적특정진행건모，병침대모형적삼수학습문제，이용변분추단산법고계모형적삼수급기후험개솔；저충방법비채양법적계산량경소，이차능구근거도상수거자동우화혼합개수，실현료모형적자동선택。최후，해산법재 Berkeley 적자연도상집상진행적실험결과여경전적도상분할산법진행료비교，결과표명차방법득도적도상분할결과정도교고，구유교호적성능。
Gaussian mixture model (GMM) has been effectively used in image segmentation. In this case, the features of an image are described by a mixture model with K different components. However, how to choose the number of mixture components K and estimate model parameters are still short of solutions. Current algorithms such as maximum likelihood and sampling methods are known for their own limitations. So we present an alternative algorithm based on Bayesian variational method and apply it in image segmentation. This method works at less computational cost than sampling methods, and can also naturally handle the model selection problem. In the model’s iterative process, the algorithm can automatically determine the number of mixture components in view of the data collected. By comparing our method against other classical segmentation methods on natural images acquired from Berkeley Segmentation Data Set, it suggests that our method provides better performance on image segmentation.
