基于邊緣盒與低秩背景的圖像顯著區(qū)域檢測算法
發(fā)布時間：2019-08-16 14:31
【摘要】：針對現(xiàn)有顯著性區(qū)域邊界不明確和檢測效果魯棒性較差等問題,提出了一種新穎的圖像顯著區(qū)域檢測方法,該方法結合了邊緣盒粗定位和低秩背景模型細篩選來提高顯著區(qū)域的檢測性能。首先,對基于邊緣盒的圖像顯著區(qū)域檢測方法進行改進,采用OTSU方法自適應計算邊緣模值的最佳分割閾值,以替代固定分割閾值,降低邊界點檢測誤差;其次,在基于邊緣盒檢測到的可疑顯著區(qū)域上,采用魯棒主成分分析方法獲取圖像的低秩分量,構建背景模型,并基于背景差分方法剔除背景區(qū)域,減少顯著區(qū)域的虛檢現(xiàn)象。在PASCAL VOC 2007數(shù)據(jù)集上的實驗結果表明,提出的方法明顯提高了顯著區(qū)域檢測的精確度和召回率,同時具有較高的檢測效率。
[Abstract]:In order to solve the problems of unclear boundary of salient region and poor robustness of detection effect, a novel image salient region detection method is proposed, which combines edge box rough location and low rank background model fine screening to improve the detection performance of significant region. Firstly, the image salient area detection method based on edge box is improved, and the OTSU method is used to calculate the optimal segmentation threshold of edge modulus adaptively, in order to replace the fixed segmentation threshold and reduce the detection error of boundary points. Secondly, in the suspicious significant area based on edge box detection, robust principal component analysis (PCA) is used to obtain the low rank component of the image, the background model is constructed, and the background region is eliminated based on the background difference method to reduce the false detection of the significant area. The experimental results on PASCAL VOC 2007 data set show that the proposed method obviously improves the accuracy and recall rate of significant area detection, and has high detection efficiency.
【作者單位】： 江蘇師范大學計算機學院;太原理工大學電氣與動力工程學院;中國礦業(yè)大學計算機科學與技術學院;
【基金】：江蘇省教育科學“十二五”規(guī)劃課題(C-c/2011/02/010) 江蘇省教育科學“十二五”規(guī)劃2013年度立項課題(D/2013/02/273)的階段性成果 山西省重大專項項目(20131101029)資助
【分類號】：TP391.41
[Abstract]:In order to solve the problems of unclear boundary of salient region and poor robustness of detection effect, a novel image salient region detection method is proposed, which combines edge box rough location and low rank background model fine screening to improve the detection performance of significant region. Firstly, the image salient area detection method based on edge box is improved, and the OTSU method is used to calculate the optimal segmentation threshold of edge modulus adaptively, in order to replace the fixed segmentation threshold and reduce the detection error of boundary points. Secondly, in the suspicious significant area based on edge box detection, robust principal component analysis (PCA) is used to obtain the low rank component of the image, the background model is constructed, and the background region is eliminated based on the background difference method to reduce the false detection of the significant area. The experimental results on PASCAL VOC 2007 data set show that the proposed method obviously improves the accuracy and recall rate of significant area detection, and has high detection efficiency.
【作者單位】： 江蘇師范大學計算機學院;太原理工大學電氣與動力工程學院;中國礦業(yè)大學計算機科學與技術學院;
【基金】：江蘇省教育科學“十二五”規(guī)劃課題(C-c/2011/02/010) 江蘇省教育科學“十二五”規(guī)劃2013年度立項課題(D/2013/02/273)的階段性成果 山西省重大專項項目(20131101029)資助
【分類號】：TP391.41
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