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基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法

韓錚 肖志濤

韓錚, 肖志濤. 基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法[J]. 電子與信息學(xué)報(bào), 2018, 40(3): 610-617. doi: 10.11999/JEIT170472
引用本文: 韓錚, 肖志濤. 基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法[J]. 電子與信息學(xué)報(bào), 2018, 40(3): 610-617. doi: 10.11999/JEIT170472
HAN Zheng, XIAO Zhitao. Weakly Supervised Semantic Segmentation Based on Semantic Texton Forest and Saliency Prior[J]. Journal of Electronics & Information Technology, 2018, 40(3): 610-617. doi: 10.11999/JEIT170472
Citation: HAN Zheng, XIAO Zhitao. Weakly Supervised Semantic Segmentation Based on Semantic Texton Forest and Saliency Prior[J]. Journal of Electronics & Information Technology, 2018, 40(3): 610-617. doi: 10.11999/JEIT170472

基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法

doi: 10.11999/JEIT170472
基金項(xiàng)目: 

高等學(xué)校博士學(xué)科點(diǎn)專項(xiàng)科研基金(SRFDP 20131201110001),中國(guó)紡織工業(yè)協(xié)會(huì)應(yīng)用基礎(chǔ)研究項(xiàng)目(J201509),內(nèi)蒙古自治區(qū)高等學(xué)??茖W(xué)技術(shù)研究項(xiàng)目(NJZY237)

Weakly Supervised Semantic Segmentation Based on Semantic Texton Forest and Saliency Prior

Funds: 

The Specialized Research Fund for the Doctoral Program of Higher Education (SRFDP20131201110001), The Applied Basic Research Programs of China National Textile and Apparel Council (J201509), The Scientific Studies Program of Higher Education of Inner Mongolia Municipality (NJZY237)

  • 摘要: 弱監(jiān)督語(yǔ)義分割任務(wù)常利用訓(xùn)練集中全體圖像的超像素及其相似度建立圖模型,使用圖像級(jí)別標(biāo)記的監(jiān)督關(guān)系進(jìn)行約束求解。全局建模缺少單幅圖像結(jié)構(gòu)信息,同時(shí)此類參數(shù)方法受到復(fù)雜度限制,無(wú)法使用大規(guī)模的弱監(jiān)督訓(xùn)練數(shù)據(jù)。針對(duì)以上問題,該文提出一種基于紋元森林和顯著性先驗(yàn)的弱監(jiān)督圖像語(yǔ)義分割方法。算法使用弱監(jiān)督數(shù)據(jù)和圖像顯著性訓(xùn)練隨機(jī)森林分類器用于語(yǔ)義紋元森林特征(Semantic Texton Forest, STF)的提取。測(cè)試時(shí),先將圖像進(jìn)行過分割,然后提取超像素語(yǔ)義紋元特征,利用樸素貝葉斯法進(jìn)行超像素標(biāo)記的概率估計(jì),最后在條件隨機(jī)場(chǎng)(CRF)框架下結(jié)合圖像顯著性信息定義了新的能量函數(shù)表達(dá)式,將圖像的標(biāo)注(labeling)問題轉(zhuǎn)換為能量最小化問題求解。在MSRC-21類數(shù)據(jù)庫(kù)上進(jìn)行了驗(yàn)證,完成了語(yǔ)義分割任務(wù)。結(jié)果表明,在并未對(duì)整個(gè)訓(xùn)練集建立圖模型的情況下,僅利用單幅圖像的顯著性信息也可以得到較好的分割結(jié)果,同時(shí)非參模型有利于規(guī)模數(shù)據(jù)分析。
  • KOHLI Pushmeet, LADICKY L, and TORR P H S. Robust higher order potentials for enforcing label consistency[J]. International Journal of Computer Vision, 2009, 82(3): 302-324. doi: 10.1007/s11263-008-0202-0.
    ZHANG L, SONG M, LIU Z, et al. Probabilistic graphlet cut: Exploiting spatial structure cue for weakly supervised image segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 1908-1915. doi: 10.1109/CVPR.2013.249.
    ZHANG Ke, ZHANG Wei, ZHENG Yingbin, et al. Sparse reconstruction for weakly supervised semantic segmentation [C]. International Joint Conference on Artificial Intelligence, Beijing, China, 2013: 1889-1895.
    VEZHNEVETS A, FERRARIV, and BUHMANN J M. Weakly supervised structured output learning for semantic segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Providence, RI, USA, 2012: 845-852. doi: 10.1109/CVPR.2012.6247757.
    VEZHNEVETS A and BUHMANN J M. Towards weakly supervised semantic segmentation by means of multiple instance and multitask learning[C]. IEEE Conference on Computer Vision and Pattern Recognition, San Francisco, CA, USA, 2010: 3249-3256. doi: 10.1109/CVPR.2010. 5540060.
    SHOTTON Jamie, JOHNSON Matthew, and CIPOLLA Roberto. Semantic texton forests for image categorization and segmentation[C]. IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, AK, USA, 2008: 1-8. doi: 10.1109/CVPR.2008.4587503.
    WEI Yunchao, LIANG Xiaodan, CHEN Yunpeng, et al. STC: a simple to complex framework for weakly-supervised semantic segmentation[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2016, 39(11): 2314-2320. doi: 10.1109/TPAMI.2016.2636150.
    VEZHNEVETS A, FERRARI V, and BUHMANN J. M. Weakly supervised semantic segmentation with a multi-image model[C]. IEEE International Conference on Computer Vision, Washington, DC, USA, 2011: 643-650. doi: 10.1109/ ICCV.2011.6126299.
    ZENG Zinan, XIAO Shijie, JIA Kui, et al. Learning by associating ambiguously labeled images[C]. IEEE Conference on Computer Vision and Pattern Recognition, Portland, OR, USA, 2013: 708-715. doi: 10.1109/CVPR.2013.97.
    VEZHNEVETS A, BUHMANN J M, and FERRARI V. Active learning for semantic segmentation with expected change[C]. IEEE Conference on Computer Vision and Pattern Recognition, Washington, DC, USA, 2012: 3162-3169. doi: 10.1109/CVPR.2012.6248050.
    YING P, LIU J, and LU H. Dictionary learning based superpixels clustering for weakly-supervised semantic segmentation[C]. IEEE International Conference on Image Processing, Quebec City, QC, Canada, 2015: 4258-4262. doi: 10.1109/ICIP.2015.7351609.
    OQUAB Maxime, BOTTOU Leon, LAPTEV Ivan, et al. Is object localization for free? Weakly-supervised learning with convolutional neural networks[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA, 2015: 685-694. doi: 10.1109/CVPR.2015.7298668.
    PAPANDREOU George, CHEN Liang-chieh, MURPHY Kevin, et al. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation[C]. IEEE International Conference on Computer Vision, Santiago, Chile, 2015: 1742-1750, doi: 10.1109/ICCV.2015. 203.
    PINHEIRO Pedro O and COLLOBERT Ronan. From image- level to pixel-level labeling with Convolutional Networks[C]. IEEE Conference on Computer Vision and Pattern Recognition, Boston, Massachusetts, USA, 2015: 1713-1721. doi: 10.1109/CVPR.2015.7298780.
    XU Jia, SCHWING A G, and URTASUN R. Tell me what you see and i will show you where it is[C]. IEEE Conference on Computer Vision and Pattern Recognition, Columbus, OH, USA, 2014: 3190-3197. doi: 10.1109/CVPR.2014.408.
    CABRAL R, TORRE F D L, COSTEIRA J P, et al. Matrix completion for weakly-supervised multi-label image classification[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2015, 37(1): 121-135. doi: 10.1109/ TPAMI.2014.2343234.
    KOLMOGOROV Vladimir and ZABIH R. What energy functions can be minimized via graph cuts?[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(2): 147-159. doi: 10.1109/TPAMI.2004.1262177.
    GEURTS Pierre, DAMIEN Ernst, and LOUIS Wehenkel. Extremely randomized trees[J]. Machine Learning, 2006, 63(1): 3-42. doi: 10.1007/s10994-006-6226-1.
    JIANG Huaizu, WANG Jingdong, YUAN Zejian, et al. Salient object detection: A discriminative regional feature integration approach[J]. International Journal of Computer Vision, 2016, 9(4): 1-18. doi: 10.1007/s11263-016-0977-3.
    GOFERMAN Stas, ZELNIK-MANOR Lihi, and TAL Ayellet. Context-aware saliency detection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 34(10): 1915-1926. doi: 10.1109/TPAMI.2011.272.
    SHOTTON Jamie, WINN John, ROTHER Carsten, et al. Texton Boost: Joint appearance, shape and context modeling for multi-class object recognition and segmentation[C]. European Conference on Computer Vision, Graz, Austria, 2006: 1-15. doi: 10.1007/11744023-1.
    LEVINSHTEIN A, STERE A, KUTULAKOS K N, et al. TurboPixels: fast superpixels using geometric flows[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009, 31(12): 2290-2297. doi: 10.1109/TPAMI.2009.96.
    LADICKY L, RUSSELL C, KOHLI P, et al. Associative hierarchical random fields[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2014, 36(6): 1056-1077. doi: 10.1109/TPAMI.2013.165.
    VERBEEK J and TRIGGS B. Region classification with markov field aspect models[C]. IEEE Conference on Computer Vision and Pattern Recognition, Minneapolis, MN, USA, 2007: 1-8. doi: 10.1109/CVPR.2007.383098.
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出版歷程
  • 收稿日期:  2017-05-17
  • 修回日期:  2017-11-27
  • 刊出日期:  2018-03-19

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