基于半监督线性近邻传递的相关反馈方法

黄传波, 金忠

黄传波, 金忠. 基于半监督线性近邻传递的相关反馈方法[J]. 信息与控制, 2011, 40(3): 289-295.
引用本文: 黄传波, 金忠. 基于半监督线性近邻传递的相关反馈方法[J]. 信息与控制, 2011, 40(3): 289-295.
HUANG Chuanbo, JIN Zhong. Relevance Feedback Algorithm Based on Semi-supervised Linear Neighborhood Propagation[J]. INFORMATION AND CONTROL, 2011, 40(3): 289-295.
Citation: HUANG Chuanbo, JIN Zhong. Relevance Feedback Algorithm Based on Semi-supervised Linear Neighborhood Propagation[J]. INFORMATION AND CONTROL, 2011, 40(3): 289-295.

基于半监督线性近邻传递的相关反馈方法

基金项目: 国家自然科学基金资助项目(60873151,60973098,90820306)
详细信息
    作者简介:

    黄传波(1972- ),男,博士生.研究领域为模式识别与图像处理等.
    金忠(1961- ),男,博士,教授,博士生导师.研究领域为模式识别,图像处理等.

    通讯作者:

    黄传波, huangjunfengcq@126.com

  • 中图分类号: TP118

Relevance Feedback Algorithm Based on Semi-supervised Linear Neighborhood Propagation

  • 摘要: 提出了一种半监督线性近邻传递的相关反馈方法FSLNP(feedback semi-supervised linear neighborhood propagation).该算法不仅能够保持正、负例约束信息,而且能够保持图的局部以及全局相关性结构信息.采用相关反馈的有标签和未知标签图像点,找到比较好的表示图像相关性的一个图结构,来揭示图像点的语义间结构关系.实验结果表明:该算法可以提高检索的准确度,而且在经过长期学习后可以获得一个优化相关性的图结构.
    Abstract: A feedback semi-supervised linear neighborhood propagation method(FSLNP) is proposed.FSLNP method can not only preserve the positive and negative constraints but also preserve the local and global relevance structure information of the whole graph.With both labeled and unlabeled images in relevance feedbacks,a better structure for relevance representation among images is found to reveal the semantic structure.Experimental results show that FSLNP can effectively improve retrieval accuracy,and after long term learning,an optimal relevance graph space can be obtained.
  • [1] Smeulders A W M,Worring M,Santini S,et al.Content-based image retrieval at the end of the early years[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2000,22(12):1349-1380.
    [2] Zhou X S,Huang T S.Relevance feedback in image retrieval:A comprehensive review[J].Multimedia Systems,2003,8(6):536-544.
    [3] Chapelle O,Scholkopf B,Zien A.Semi-supervised learning[M].Cambrige,MA,USA:MIT Press,2006.
    [4] Wu Y,Tian Q,Huang T.Discriminant-EM algorithm with application to image retrieval[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.Piscataway,NJ,USA:IEEE,2000:222-227.
    [5] Sia K C,King I.Relevance feedback based on parameter estimation of target distribution[C]//Proceedings of the International Joint Conference on Neural Networks.Piscataway,NJ,USA:IEEE.2002:1974-1979.
    [6] 鲁坷,赵继东,叶娅兰,等.一种用于图像检索的新型半监督学习算法[J].电子科技大学学报,2005,34(5):669-671.Lu K,Zhao J D,Ye Y L,et al.Algorithm for semi-supervised learning in image retrieval[J].Journal of University of Elec tronic Science and Technology of China,2005,34(5):669-671.
    [7] Blum A,Mitchell T.Combining labeled and unlabeled data with co-training[C]//Proceedings of the Annual ACM Conference on Computational Learning Theory.New York,NJ,USA:ACM,1998:92-100.
    [8] Zhou Z H,Li M.Tri-training:Exploiting unlabeled data using three classifiers[J].IEEE Transactions on Knowledge and Data Engineering,2005,17(11):1529-1541.
    [9] E1-Yaniv R,Pechyony D,Vapnik V.Large margin vs.large volume in transductive learning[J].Machine Learning,2008,72(3):173-188.
    [10] 薛贞霞,刘三阳,刘万里.基于SVDD的渐进直推式支持向量机学习算法[J].模式识别与人工智能,2008,21(6):721-727.Xue Z X,Liu S Y,Liu W L.SVDD based learning algorithm with progressive transductive support vector machines[J].Pattern Recognition and Artificial Intelligence,2008,21(6):721-727.
    [11] Tong S,Chang E.Support vector machine active learning for image retrieval[C]//Proceedings of the ACM International Conference on Multimedia.New York,NJ,USA:ACM,2002:107-118.
    [12] Zhou Z H,Chen K J,Dai H B.Enhancing relevance feedback in image retrieval using unlabeled data[J].ACM Transactions on Information Systems,2006,24(2):219-244.
    [13] Wang F,Zhang C S.Label propagation through linear neighborhoods[J].IEEE Transactions on Knowledge and Data Engineering,2008,20(1):55-67.
    [14] Zhu X,Ghahramani Z.Learning from labeled and unlabeled data with label propagation[R].Pittsburgh,PA,USA:Carnegie Mellon University,2002.
    [15] 王和勇,郑杰,姚正安,等.基于聚类和改进距离的LLE方法在数据降维中的应用[J].计算机研究与发展,2006,43(8):1485-1490.Wang H Y,Zheng J,Yao Z A,et al.Application of dimension reduction on using improved LLE based on clustering[J].Journal of Computer Research and Development,2006,43(8):1485-1490.
    [16] Geng X,Zhan D C,Zhou Z H.Supervised nonlinear dimensionality reduction for visualization and classification[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2005,35(6):1098-1107.
    [17] Manjunath B S,Ohm J R,Vasudevan V V,et al.Color and texture descriptors[J].IEEE Transactions on Circuits and Systems for Video Technology,2001,11(6):703-715.
    [18] Stricker M A,Orengo M.Similarity of color images[C]//Proceedings of SPIE-The International Society for Optical Engineering.Bellingham,WA,USA:Society of Photo-Optical Insurumentation Engineers,1995:381-392.
    [19] Pinheiro A M G.Image description using scale-space edge pixel directions histogram[C]//Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization.Piscataway,NJ,USA:IEEE,2007:211-218
    [20] Lin Y Y,Liu T L,Chen H T.Semantic manifold learning for image retrieval[C]//Proceedings of the Annual ACM International Conference on Multimedia.New York,NJ,USA:ACM,2005:249-258.
    [21] Yu J,Tian Q.Learning image manifolds by semantic subspace projection[C]//Proceedings of the Annual ACM International Conference on Multimedia.New York,NJ,USA:ACM,2006:297-306.
计量
  • 文章访问数:  1626
  • HTML全文浏览量:  0
  • PDF下载量:  101
  • 被引次数: 0
出版历程
  • 收稿日期:  2010-02-07
  • 发布日期:  2011-06-19

目录

    /

    返回文章
    返回
    x