基于模糊聚類的膚色分割
Skin Extraction Based on Fuzzy Cluster
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摘要: 膚色是彩色圖像人臉檢測中一個非常重要的特征。通常采用一個統(tǒng)計模型分割出可能的膚色區(qū)域，但往往會有很多誤判。此外，CbCr等簡單的二維空間，不能表示真正的膚色分布。該文提出采用三維的CrCbCg模型來更精確地描述膚色分布，同時考慮到一幅圖像中膚色區(qū)域內(nèi)顏色點的分布具有相對穩(wěn)定的特點，利用一種模糊聚類的方法對CrCbCg模型的輸出結(jié)果進行二次分割，進一步去除非膚色點。由于結(jié)合了每幅圖像自身的特點，該算法能大大提高膚色分割結(jié)果的準確性。大量實驗結(jié)果表明，該算法能有效處理95%以上的彩色圖像，對于70%以上的圖像可得到很好的分割結(jié)果。
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關(guān)鍵詞:
- 人臉檢測;膚色模型;聚類分割
Abstract: Skin color is an important feature for face detection in color images. Usually by applying a statistical skin model, possible skin region can be segmented. However, the output can not be as accurate as expected. Besides, simple 2D model, e.g. CbCr, can not present the real skin point distribution. So this paper presents a 3D CrCbCg model to describe skin distribution more precisely. Meanwhile, considering skin points in a specific image have a relative stable distribution, a cluster-based skin model is presented to remove background points which are wrongly retained by the general model. Because of applying the characteristic of each specific image, this algorithm can effectively improve the performance and accuracy of skin model. Experimental result shows that this algorithm can achieve satisfied results for over 95% images, including obviously improved images for over 70% . -
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