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基于卷積神經(jīng)網(wǎng)絡(luò)與全局優(yōu)化的協(xié)同顯著性檢測

吳澤民,  王軍,  胡磊,  田暢,  曾明勇,  杜麟

吳澤民, 王軍, 胡磊, 田暢, 曾明勇, 杜麟. 基于卷積神經(jīng)網(wǎng)絡(luò)與全局優(yōu)化的協(xié)同顯著性檢測[J]. 電子與信息學(xué)報(bào), 2018, 40(12): 2896-2904. doi: 10.11999/JEIT180241
引用本文: 吳澤民, 王軍, 胡磊, 田暢, 曾明勇, 杜麟. 基于卷積神經(jīng)網(wǎng)絡(luò)與全局優(yōu)化的協(xié)同顯著性檢測[J]. 電子與信息學(xué)報(bào), 2018, 40(12): 2896-2904. doi: 10.11999/JEIT180241
Zemin WU, Jun WANG, Lei HU, Chang TIAN, Mingyong ZENG, Lin DU. Co-saliency Detection Based on Convolutional Neural Network and Global Optimization[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2896-2904. doi: 10.11999/JEIT180241
Citation: Zemin WU, Jun WANG, Lei HU, Chang TIAN, Mingyong ZENG, Lin DU. Co-saliency Detection Based on Convolutional Neural Network and Global Optimization[J]. Journal of Electronics & Information Technology, 2018, 40(12): 2896-2904. doi: 10.11999/JEIT180241

基于卷積神經(jīng)網(wǎng)絡(luò)與全局優(yōu)化的協(xié)同顯著性檢測

doi: 10.11999/JEIT180241

  • 中國人民解放軍陸軍工程大學(xué)通信工程學(xué)院 ??南京 ??210007

詳細(xì)信息
    作者簡介:

    吳澤民：男，1973年生，副教授，碩士生導(dǎo)師，研究方向?yàn)閳D像分析、數(shù)據(jù)融合

    王軍：男，1995年生，碩士生，研究方向?yàn)樯疃葘W(xué)習(xí)、圖像與視頻的顯著度研究

    胡磊：男，1987年生，博士，研究方向?yàn)槟繕?biāo)跟蹤與識別、數(shù)據(jù)融合

    田暢：男，1963年生，教授，博士生導(dǎo)師，研究方向?yàn)閿?shù)據(jù)鏈技術(shù)、圖像視頻處理

    曾明勇：男，1988年生，博士生，研究方向?yàn)槟繕?biāo)檢測與識別

    杜麟：男，1990年生，博士生，研究方向?yàn)橐曨l編碼與視頻傳輸保障

    通訊作者:

    王軍　 wangjun_ice@126.com

  • 中圖分類號: TP391.41

Co-saliency Detection Based on Convolutional Neural Network and Global Optimization

  • College of Communications Engineering, Army Engineering University of PLA, Nanjing 210007, China

  • 摘要: 針對目前協(xié)同顯著性檢測問題中存在的協(xié)同性較差、誤匹配和復(fù)雜場景下檢測效果不佳等問題，該文提出一種基于卷積神經(jīng)網(wǎng)絡(luò)與全局優(yōu)化的協(xié)同顯著性檢測算法。首先基于VGG16Net構(gòu)建了全卷積結(jié)構(gòu)的顯著性檢測網(wǎng)絡(luò)，該網(wǎng)絡(luò)能夠模擬人類視覺注意機(jī)制，從高級語義層次提取一幅圖像中的顯著性區(qū)域；然后在傳統(tǒng)單幅圖像顯著性優(yōu)化模型的基礎(chǔ)上構(gòu)造了全局協(xié)同顯著性優(yōu)化模型。該模型通過超像素匹配機(jī)制，實(shí)現(xiàn)當(dāng)前超像素塊顯著值在圖像內(nèi)與圖像間的傳播與共享，使得優(yōu)化后的顯著圖相對于初始顯著圖具有更好的協(xié)同性與一致性。最后，該文創(chuàng)新性地引入圖像間顯著性傳播約束因子來克服超像素誤匹配帶來的影響。在公開測試數(shù)據(jù)集上的實(shí)驗(yàn)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。
    Abstract: To solve the problems in current co-saliency detection algorithms, a novel co-saliency detection algorithm is proposed which applies fully convolution neural network and global optimization model. First, a fully convolution saliency detection network is built based on VGG16Net. The network can simulate the human visual attention mechanism and extract the saliency region in an image from the semantic level. Second, based on the traditional saliency optimization model, the global co-saliency optimization model is constructed, which realizes the transmission and sharing of the current superpixel saliency value in inter-images and intra-image through superpixel matching, making the final saliency map has better co-saliency value. Third, the inter-image saliency value propagation constraint parameter is innovatively introduced to overcome the disadvantages of superpixel mismatching. Experimental results on public test datasets show that the proposed algorithm is superior over current state-of-the-art methods in terms of detection accuracy and detection efficiency, and has strong robustness.
    • HTML全文

  • 圖  1  顯著性檢測網(wǎng)絡(luò)結(jié)構(gòu)示意圖

    圖  2  本文算法每個(gè)步驟所生成的顯著圖對比

    圖  3  協(xié)同顯著性優(yōu)化示意圖

    圖  5  iCoSeg數(shù)據(jù)集上部分實(shí)驗(yàn)結(jié)果對比示例

    圖  4  ImgPair數(shù)據(jù)集上部分實(shí)驗(yàn)結(jié)果對比示例(GT表示真值圖)

    圖  6  本文算法在iCoSeg數(shù)據(jù)集上的量化分析(F-measure曲線圖)

    圖  8  本文算法與其他算法在兩大數(shù)據(jù)集上的F-measure曲線對比

    圖  7  本文算法與其他算法在兩大數(shù)據(jù)集上的P-R曲線對比

    表  1  不同算法在兩大數(shù)據(jù)庫上的測試結(jié)果對比

    算法 ImgPair iCoSeg
    AUC AF MAE AUC AF MAE
    SA 0.967 0.826 0.160 0.965 0.720 0.160
    HS 0.954 0.821 0.147 0.954 0.640 0.180
    CB-C 0.931 0.782 0.178 0.913 0.647 0.198
    CB-S 0.927 0.749 0.181 0.935 0.688 0.173
    ACM 0.880 0.719 0.197 – – –
    SM 0.879 0.724 0.166 0.621 0.580 0.234
    LDW – – – 0.957 0.699 0.178
    IPIM – – – 0.964 0.703 0.159
    本文CNN 0.958 0.811 0.098 0.932 0.761 0.081
    本文CNN+COOPT 0.981 0.904 0.075 0.962 0.848 0.056
    下載: 導(dǎo)出CSV

    表  2  不同協(xié)同顯著性算法平均運(yùn)算時(shí)間比較

    算法 CB-C SA SM 本文CNN 本文CNN+COOPT
    時(shí)間(s) 5.40 2.10 6.60 0.12 2.70
    處理器 CPU CPU CPU GPU CPU+GPU
    下載: 導(dǎo)出CSV
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  • 收稿日期:  2018-03-16
  • 修回日期:  2018-08-22
  • 網(wǎng)絡(luò)出版日期:  2018-08-31
  • 刊出日期:  2018-12-01

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