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LI Chunhua,QIN Yunfan,LIU Yukun.Bayesian model saliency detection algorithm based on improved convex hull[J].Journal of Hebei University of Science and Technology,2021,42(1):30-37
改進凸包的貝葉斯模型顯著性檢測算法
Bayesian model saliency detection algorithm based on improved convex hull
Received:September 30, 2020  Revised:November 26, 2020
DOI:10.7535/hbkd.2021yx01005
中文關鍵詞:  圖像處理  顯著性檢測  凸包  超像素  流行排序  貝葉斯模型
英文關鍵詞:image processing  significance detection  convex hull  superpixel  manifold ranking algorithm  Bayesian model
基金項目:河北省人力資源和社會保障廳引進留學人員資助項目(C201811)
Author NameAffiliationE-mail
LI Chunhua School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
QIN Yunfan School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 1960658657@qq.com 
LIU Yukun School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang  
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中文摘要:
      針對傳統貝葉斯模型算法對圖像顯著區域檢測精度需要進一步提高的問題,提出一種改進凸包的貝葉斯模型顯著性檢測算法。首先,利用流行排序算法對圖像進行前景提取,提取的前景區域作為貝葉斯模型的先驗概率;其次,利用顏色增強的Harris角點檢測算法檢測圖像在RGB,HSV,CIELab 3個顏色空間中的特征點,分別構造RGB,HSV,CIELab空間的凸包,求取3個顏色空間下的凸包的交集;再次,通過貝葉斯模型根據先驗概率、凸包與顏色直方圖結合得到的觀測似然概率計算獲得顯著性區域圖;最后,將新算法在兩大公開數據集MSRA和ECSSD中進行測試。結果表明,新算法能夠有效抑制背景噪聲,完整檢出顯著區域,F-measure值在MSRA和ECSSD數據庫中的測試結果分別為0.87和0.71,準確率-召回率曲線在復雜圖像數據庫高于傳統經典算法。新算法改進了傳統經典算法的檢測效果,進一步提高了顯著圖檢測的準確性。
英文摘要:
      Aiming at the problem of poor precision performance of traditional Bayesian model saliency detection algorithm, a Bayesian model saliency detection algorithm based on improved convex hull was proposed. Firstly, the foreground of the image was extracted by the manifold ranking algorithm, which was used as the prior probability in Bayesian model. Secondly, Harris corner detection algorithm based on color enhancement was used to detect the feature points of the image in three color spaces of RGB, HSV and CIELab; the convex hulls in RGB, HSV and CIELab spaces were constructed respectively; and the intersection of convex hulls were obtained. Thirdly, the saliency region map was calculated by Bayesian model according to the prior probability and the observed likelihood probability obtained by combining convex hulls and color histograms. Finally, the proposed algorithm was tested in two public data sets MSRA and ECSSD. The experimental results show that the proposed algorithm can suppress the background noise effectively and detect the salient areas completely. The test results of F-measure value in MSRA and ECSSD databases are 0.87 and 0.71 respectively, and the accuracy-recall rate curve is higher than that of traditional classical algorithms in complex image databases. The proposed algorithm improves the detection effect of the traditional classical algorithm and the accuracy of saliency map detection.
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