Loading [MathJax]/jax/output/HTML-CSS/jax.js

一级黄色片免费播放|中国黄色视频播放片|日本三级a|可以直接考播黄片影视免费一级毛片

高級搜索

留言板

尊敬的讀者、作者、審稿人, 關(guān)于本刊的投稿、審稿、編輯和出版的任何問題, 您可以本頁添加留言。我們將盡快給您答復(fù)。謝謝您的支持!

姓名
郵箱
手機(jī)號碼
標(biāo)題
留言內(nèi)容
驗證碼

基于加權(quán)的K近鄰線性混合顯著性目標(biāo)檢測

李煒 李全龍 劉政怡

李煒, 李全龍, 劉政怡. 基于加權(quán)的K近鄰線性混合顯著性目標(biāo)檢測[J]. 電子與信息學(xué)報, 2019, 41(10): 2442-2449. doi: 10.11999/JEIT190093
引用本文: 李煒, 李全龍, 劉政怡. 基于加權(quán)的K近鄰線性混合顯著性目標(biāo)檢測[J]. 電子與信息學(xué)報, 2019, 41(10): 2442-2449. doi: 10.11999/JEIT190093
Wei LI, Quanlong LI, Zhengyi LIU. Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2442-2449. doi: 10.11999/JEIT190093
Citation: Wei LI, Quanlong LI, Zhengyi LIU. Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending[J]. Journal of Electronics & Information Technology, 2019, 41(10): 2442-2449. doi: 10.11999/JEIT190093

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
詳細(xì)信息
    作者簡介:

    李煒:女,1969年生,教授,研究方向為計算機(jī)視覺

    李全龍:男,1995年生,碩士生,研究方向為圖像顯著性檢測

    劉政怡:女,1978年生,副教授,研究方向為計算機(jī)視覺

    通訊作者:

    劉政怡 liuzywen@ahu.edu.cn

  • 中圖分類號: TP391

Salient Object Detection Using Weighted K-nearest Neighbor Linear Blending

  • 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。
  • 圖  1  本文方法的框架圖

    圖  2  強顯著模型示意圖

    圖  3  加權(quán)k近鄰模型示意圖

    圖  4  m取不同值在ASD數(shù)據(jù)集上的F-measure曲線

    圖  5  n取不同值在ASD數(shù)據(jù)集上的F-measure曲線

    圖  6  各種方法產(chǎn)生的顯著圖的視覺對比

    圖  7  各方法及其提高在ASD和DUT-OMRON數(shù)據(jù)集上的P-R曲線

    圖  8  WKNNLB和BLSVM在ASD和DUT-OMRON數(shù)據(jù)集上的P-R曲線

    表  1  特征fji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]
    6~64LBP直方圖值59[0,1]
    下載: 導(dǎo)出CSV

    表  2  5種經(jīng)典方法及其提高在F-度量值的對比

    算法ITIT+LRMRLRMR+GCGC+MRMR+MBDMBD+
    ASD0.3810.5420.7270.8290.8110.8480.8690.8760.8550.867
    DUT-OMRON0.3430.5410.4980.5450.5200.5540.5740.5760.8500.854
    下載: 導(dǎo)出CSV

    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

    ASDSED2PASCAL-SDUT-OMRON
    F-measureTimeF-measureTimeF-measureTimeF-measureTime
    WKNNLB0.82040580.7583320.65550000.53430864
    BLSVM0.81080930.7407200.651111840.52465120
    下載: 導(dǎo)出CSV
  • BORJI A and ITTI L. State-of-the-art in visual attention modeling[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2013, 35(1): 185–207. doi: 10.1109/TPAMI.2012.89
    ITTI L. Automatic foveation for video compression using a neurobiological model of visual attention[J]. IEEE Transactions on Image Processing, 2004, 13(10): 1304–1318. doi: 10.1109/TIP.2004.834657
    ZHANG Fan, DU Bo, and ZHANG Liangpei. Saliency-guided unsupervised feature learning for scene classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(4): 2175–2184. doi: 10.1109/TGRS.2014.2357078
    LU Xiaoqiang, ZHENG Xiangtao, and LI Xuelong. Latent semantic minimal hashing for image retrieval[J]. IEEE Transactions on Image Processing, 2017, 26(1): 355–368. doi: 10.1109/TIP.2016.2627801
    WEI Yunchao, XIAO Huaxin, SHI Honghui, et al. Revisiting dilated convolution: A simple approach for weakly-and semi-supervised semantic segmentation[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 7268–7277. doi: 10.1109/CVPR.2018.00759.
    ZHANG Xiaoning, WANG Tiantian, QI Jinqing, et al. Progressive attention guided recurrent network for salient object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 714–722. doi: 10.1109/CVPR.2018.00081.
    CHEN Shuhan, TAN Xiuli, WANG Ben, et al. Reverse attention for salient object detection[C]. The 15th European Conference on Computer Vision, Munich, Germany, 2018: 236–252. doi: 10.1007/978-3-030-01240-3_15.
    ZHANG Lu, DAI Ju, LU Huchuan, et al. A bi-directional message passing model for salient object detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 1741–750. doi: 10.1109/CVPR.2018.00187.
    WANG Tiantian, ZHANG Lihe, WANG Shuo, et al. Detect globally, refine locally: A novel approach to saliency detection[C]. 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, Salt Lake City, USA, 2018: 3127–3135. doi: 10.1109/CVPR.2018.00330.
    HOU Qibin, CHENG Mingming, HU Xiaowei, et al. Deeply supervised salient object detection with short connections[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019, 41(4): 815–828. doi: 10.1109/TPAMI.2018.2815688
    YANG Chuan, ZHANG Lihe, LU Huchuan, et al. Saliency detection via graph-based manifold ranking[C]. 2013 IEEE Conference on Computer Vision and Pattern Recognition, Portland, USA, 2013: 3166–3173. doi: 10.1109/CVPR.2013.407.
    CHENG Mingming, WARRELL J, LIN Wenyan, et al. Efficient salient region detection with soft image abstraction[C]. 2013 IEEE International Conference on Computer Vision, Sydney, Australia, 2013: 1529–1536. doi: 10.1109/ICCV.2013.193.
    ZHANG Jianming, SCLAROFF S, LIN Zhe, et al. Minimum barrier salient object detection at 80 FPS[C]. 2015 IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1404–1412. doi: 10.1109/ICCV.2015.165.
    BORJI A, CHENG Mingming, JIANG Huaizu, et al. Salient object detection: A benchmark[J]. IEEE Transactions on Image Processing, 2015, 24(12): 5706–5722. doi: 10.1109/TIP.2015.2487833
    TONG Na, LU Huchuan, RUAN Xiang, et al. Salient object detection via bootstrap learning[C]. 2015 IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, 2015: 1884–1892. doi: 10.1109/CVPR.2015.7298798.
    LU Huchuan, ZHANG Xiaoning, Qi Jinqing, et al. Co-bootstrapping saliency[J]. IEEE Transactions on Image Processing, 2017, 26(1): 414–425. doi: 10.1109/TIP.2016.2627804
    SONG Hangke, LIU Zhi, DU Huan, et al. Depth-aware salient object detection and segmentation via multiscale discriminative saliency fusion and bootstrap learning[J]. IEEE Transactions on Image Processing, 2017, 26(9): 4204–4216. doi: 10.1109/TIP.2017.2711277
    WU Lishan, LIU Zhi, SONG Hangke, et al. RGBD co-saliency detection via multiple kernel boosting and fusion[J]. Multimedia Tools and Applications, 2018, 77(16): 21185–21199. doi: 10.1007/s11042-017-5576-y
    ACHANTA R, SHAJI A, SMITH K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(11): 2274–2282. doi: 10.1109/TPAMI.2012.120
    OJALA T, PIETIKAINEN M, and MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971–987. doi: 10.1109/tpami.2002.1017623
    ACHANTA R, HEMAMI S, ESTRADA F, et al. Frequency-tuned salient region detection[C]. 2009 IEEE Conference on Computer Vision and Pattern Recognition, Miami, USA, 2009: 1597–1604. doi: 10.1109/CVPR.2009.5206596.
    ITTI L, KOCH C, and NIEBUR E. A model of saliency-based visual attention for rapid scene analysis[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(11): 1254–1259. doi: 10.1109/34.730558
    SHEN Xiaohui and WU Ying. A unified approach to salient object detection via low rank matrix recovery[C]. 2012 IEEE Conference on Computer Vision and Pattern Recognition, Providence, USA, 2012: 853–860. doi: 10.1109/CVPR.2012.6247758.
  • 加載中
圖(8) / 表(3)
計量
  • 文章訪問數(shù):  2228
  • HTML全文瀏覽量:  1230
  • PDF下載量:  105
  • 被引次數(shù): 0
出版歷程
  • 收稿日期:  2019-02-01
  • 修回日期:  2019-06-03
  • 網(wǎng)絡(luò)出版日期:  2019-06-12
  • 刊出日期:  2019-10-01

目錄

    /

    返回文章
    返回