基于加權(quán)的K近鄰線性混合顯著性目標(biāo)檢測
doi: 10.11999/JEIT190093
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安徽大學(xué)計算機(jī)科學(xué)與技術(shù)學(xué)院 合肥 230601
Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending
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College of Computer Science and Technology, Anhui University, Hefei 230601, China
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摘要: 顯著性目標(biāo)檢測旨在于一個場景中自動檢測能夠引起人類注意的目標(biāo)或區(qū)域，在自底向上的方法中，基于多核支持向量機(jī)(SVM)的集成學(xué)習(xí)取得了卓越的效果。然而，針對每一張要處理的圖像，該方法都要重新訓(xùn)練，每一次訓(xùn)練都非常耗時。因此，該文提出一個基于加權(quán)的K近鄰線性混合(WKNNLB)顯著性目標(biāo)檢測方法：利用現(xiàn)有的方法來產(chǎn)生初始的弱顯著圖并獲得訓(xùn)練樣本，引入加權(quán)的K近鄰(WKNN)模型來預(yù)測樣本的顯著性值，該模型不需要任何訓(xùn)練過程，僅需選擇一個最優(yōu)的K值和計算與測試樣本最近的K個訓(xùn)練樣本的歐式距離。為了減少選擇K值帶來的影響，多個加權(quán)的K近鄰模型通過線性混合的方式融合來產(chǎn)生強的顯著圖。最后，將多尺度的弱顯著圖和強顯著圖融合來進(jìn)一步提高檢測效果。在常用的ASD和復(fù)雜的DUT-OMRON數(shù)據(jù)集上的實驗結(jié)果表明了該算法在運行時間和性能上的有效性和優(yōu)越性。當(dāng)采用較好的弱顯著圖時，該算法能夠取得更好的效果。
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關(guān)鍵詞:
- 顯著性目標(biāo)檢測 /
- 集成學(xué)習(xí) /
- 線性混合 /
- 加權(quán)的K近鄰
Abstract: Salient object detection which aims at automatically detecting what attracts human’s attention most in a scene, bootstrap learning based on Support Vector Machine(SVM) has achieved excellent performance in bottom-up methods. However, it is time-consuming for each image to be trained once based on multiple kernel SVM ensemble. So a salient object detection model via Weighted K-Nearest Neighbor Linear Blending (WKNNLB) is proposed. First of all, existing saliency detection methods are employed to generate weak saliency maps and obtain training samples. Then, Weighted K-Nearest Neighbor (WKNN) is introduced to learning salient score of samples. WKNN model needs no pre-training process, only needs selecting K value and computing saliency value by the K-nearest neighbors labels of training sample and the distances between the K-nearest neighbors training samples and the testing sample. In order to reduce the influence of selecting K value, linear blending of multi-WKNNs is applied to generating strong saliency maps. Finally, multi-scale saliency maps of weak and strong model are integrated together to further improve the detection performance. The experimental results on common ASD and complex DUT-OMRON datasets show that the algorithm is effective and superior in running time and performance. It can even perform favorable against the state-of-the-art methods when adopting better weak saliency map. -
表 1 特征
${\text{f}}_i^j$ 取值(65維)特征維度序號 特征 維度大小 取值范圍 0～2 平均RGB值 3 [0,1] 3～5 平均CIELab值 3 [0,1] 6～64 LBP直方圖值 59 [0,1] 
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表 2 5種經(jīng)典方法及其提高在F-度量值的對比
算法 IT IT+ LRMR LRMR+ GC GC+ MR MR+ MBD MBD+ ASD 0.381 0.542 0.727 0.829 0.811 0.848 0.869 0.876 0.855 0.867 DUT-OMRON 0.343 0.541 0.498 0.545 0.520 0.554 0.574 0.576 0.850 0.854
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表 3 WKNNLB和BLSVM在4個數(shù)據(jù)集上F-度量和運行時間(s)對比
ASD SED2 PASCAL-S DUT-OMRON F-measure Time F-measure Time F-measure Time F-measure Time WKNNLB 0.820 4058 0.758 332 0.655 5000 0.534 30864 BLSVM 0.810 8093 0.740 720 0.651 11184 0.524 65120
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