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基于樣本選擇的RGBD圖像協(xié)同顯著目標(biāo)檢測

劉政怡 劉俊雷 趙鵬

劉政怡, 劉俊雷, 趙鵬. 基于樣本選擇的RGBD圖像協(xié)同顯著目標(biāo)檢測[J]. 電子與信息學(xué)報, 2020, 42(9): 2277-2284. doi: 10.11999/JEIT190393
引用本文: 劉政怡, 劉俊雷, 趙鵬. 基于樣本選擇的RGBD圖像協(xié)同顯著目標(biāo)檢測[J]. 電子與信息學(xué)報, 2020, 42(9): 2277-2284. doi: 10.11999/JEIT190393
Zhengyi LIU, Junlei LIU, Peng ZHAO. RGBD Image Co-saliency Object Detection Based on Sample Selection[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2277-2284. doi: 10.11999/JEIT190393
Citation: Zhengyi LIU, Junlei LIU, Peng ZHAO. RGBD Image Co-saliency Object Detection Based on Sample Selection[J]. Journal of Electronics & Information Technology, 2020, 42(9): 2277-2284. doi: 10.11999/JEIT190393

基于樣本選擇的RGBD圖像協(xié)同顯著目標(biāo)檢測

doi: 10.11999/JEIT190393
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)
詳細(xì)信息
    作者簡介:

    劉政怡:女,1978年生,副教授,研究方向為計算機(jī)視覺、深度學(xué)習(xí)

    劉俊雷:男,1995年生,碩士生,研究方向為計算機(jī)視覺

    趙鵬:女,1976年生,副教授,研究方向為智能信息處理、機(jī)器學(xué)習(xí)

    通訊作者:

    劉政怡 liuzywen@ahu.edu.cn

  • 中圖分類號: TN911.73; TP391.4

RGBD Image Co-saliency Object Detection Based on Sample Selection

Funds: The Provincial Natural Science Foundation of Anhui(1908085MF182), The National Natural Science Foundation of China(61602004)
  • 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。
  • 圖  1  本文提出的RGBD協(xié)同顯著目標(biāo)檢測方法的框架圖

    圖  2  不同方法生成的協(xié)同顯著圖對比

    圖  3  本文算法與其他算法在兩個數(shù)據(jù)集上的P-R曲線對比

    圖  4  本文算法兩個策略在兩個數(shù)據(jù)集上的P-R曲線對比

    圖  5  RGBD CoSal150數(shù)據(jù)集不同參數(shù)的F-measure測量

    表  1  不同算法在兩個數(shù)據(jù)集上的測試結(jié)果對比

    RGBD CoSal150RGBD CoSeg183
    S-measureF-measureMAES-measureF-measureMAE
    ESCS0.6250.5870.2180.6360.4140.156
    CBCS0.5720.5820.2150.6220.3650.116
    ICFS0.7100.7640.1790.6300.4430.163
    MCL0.7660.8100.1370.6890.4880.098
    本文方法0.8490.8810.0890.7080.5020.081
    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 CoSal150RGBD CoSeg183
    S-measureF-measureMAES-measureF-measureMAE
    顏色+紋理特征0.8160.8170.1310.6610.4730.143
    無簡單圖像選擇0.8320.8370.1170.7020.4770.090
    高維語義特征+簡單圖像選擇0.8490.8810.0890.7080.5020.081
    下載: 導(dǎo)出CSV

    表  3  不同方法平均每副圖運行時間比較(s)

    方法ESCSCBCSICFSMCL本文方法
    時間2.842.4342.6741.038.76
    下載: 導(dǎo)出CSV
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出版歷程
  • 收稿日期:  2019-06-03
  • 修回日期:  2020-03-01
  • 網(wǎng)絡(luò)出版日期:  2020-06-27
  • 刊出日期:  2020-09-27

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