基于樣本選擇的RGBD圖像協(xié)同顯著目標(biāo)檢測
doi: 10.11999/JEIT190393
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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
基金項目: 安徽省自然科學(xué)基金(1908085MF182)，國家自然科學(xué)基金(61602004)
RGBD Image Co-saliency Object Detection Based on Sample Selection
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School of Computer Science and Technology, Anhui University, Hefei 230601, China
Funds: The Provincial Natural Science Foundation of Anhui(1908085MF182), The National Natural Science Foundation of China(61602004)
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摘要: 協(xié)同顯著目標(biāo)檢測的目的是在包含兩張及以上相關(guān)圖像的圖像組中檢測共同顯著的物體。該文提出一種利用機(jī)器學(xué)習(xí)的方法對協(xié)同顯著目標(biāo)進(jìn)行檢測。首先，基于4個評分指標(biāo)從圖像組中選擇部分顯著目標(biāo)易于檢測的簡單圖像，構(gòu)成簡單圖像集；接著，基于協(xié)同一致性的原則，從簡單圖像集中提取正負(fù)樣本，并用深度學(xué)習(xí)模型提取的高維語義特征表示正負(fù)樣本；再者，利用正負(fù)樣本訓(xùn)練的協(xié)同顯著分類器對圖像中的超像素進(jìn)行分類，得到協(xié)同顯著目標(biāo)區(qū)域；最后，經(jīng)過一個平滑融合的操作，得到最終的協(xié)同顯著圖。在公開數(shù)據(jù)集上的測試結(jié)果表明，所提算法在檢測精度和檢測效率上優(yōu)于目前的主流算法，并具有較強(qiáng)的魯棒性。
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關(guān)鍵詞:
- 目標(biāo)檢測 /
- 協(xié)同顯著目標(biāo) /
- RGBD圖像 /
- 深度學(xué)習(xí) /
- 分類器
Abstract: Co-saliency object detection aims to discover common and salient objects in an image group which contains two or more relevant images. In this paper, a method of using machine learning is proposed to detect co-saliency objects. Firstly, some simple images are selected to form a simple image set based on four scoring indicators. Secondly, positive and negative samples are extracted from the simple images set based on co-coherence characteristics, and high-dimensional semantic features are extracted by the deep learning model which receives RGBD four-channels input. Thirdly, the co-saliency classifier is trained by positive and negative samples, and co-saliency maps are generated by testing all the superpixels in the images by the co-saliency classifier. Finally, a smooth fusion operation is adopted to generate the final co-saliency map. Experimental results on the public benchmark dataset show that the proposed algorithm is superior to the state-of-the-art methods in terms of accuracy and efficiency, and it is robust.-
Key words:
- Object detection /
- Co-saliency object /
- RGBD images /
- Deep learning /
- Classifier
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表 1 不同算法在兩個數(shù)據(jù)集上的測試結(jié)果對比
RGBD CoSal150 RGBD CoSeg183 S-measure F-measure MAE S-measure F-measure MAE ESCS 0.625 0.587 0.218 0.636 0.414 0.156 CBCS 0.572 0.582 0.215 0.622 0.365 0.116 ICFS 0.710 0.764 0.179 0.630 0.443 0.163 MCL 0.766 0.810 0.137 0.689 0.488 0.098 本文方法 0.849 0.881 0.089 0.708 0.502 0.081 
下載: 導(dǎo)出CSV
表 2 不同模塊在兩個數(shù)據(jù)集上的測試結(jié)果對比
RGBD CoSal150 RGBD CoSeg183 S-measure F-measure MAE S-measure F-measure MAE 顏色+紋理特征 0.816 0.817 0.131 0.661 0.473 0.143 無簡單圖像選擇 0.832 0.837 0.117 0.702 0.477 0.090 高維語義特征+簡單圖像選擇 0.849 0.881 0.089 0.708 0.502 0.081
下載: 導(dǎo)出CSV
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