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基于超边相关性的图像分类方法 被引量：2

An Image Classification Method Based on Hyperedge Correlation
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摘要 传统基于超图的图像分类方法在构建超图时并未考虑各超边之间的关系,导致最终分类效果不理想.文中结合图像视觉信息和标注信息量化超边间相关性,提出一种基于超边相关性的图像分类方法,有效地将图像相关的标注信息作为判定图像类别的指标引入到图像分类中,进而对图像进行更准确的分类.在LabelMe和UIUC数据集上的实验验证该方法的有效性. Traditional hypergraph-based image classification methods overlook the correlation among hyperedges in hypergraph construction, which results in poor classification performance. A method based on hyperedge correlation is proposed in this paper. The correlation among hyperedses is quantified by combining the image vision and its corresponding tags information. The tags corresponding to the image are introduced into the image classification as indicator information and thus better classification performance is obtained. The effectiveness of the proposed method is verified by experiments conducted on datasets such as LabelMe and UIUC.
作者 徐杰 景丽萍 于剑
机构地区 北京交通大学计算机与信息技术学院
出处 《模式识别与人工智能》 EI CSCD 北大核心 2014年第2期120-126,共7页 Pattern Recognition and Artificial Intelligence
基金 国家自然科学基金(No.61033013 61370129 61375062) 北京市自然科学基金(No.4112046) 教育部博士点基金(No.20120009110006) 中央高校基本科研业务费专项基金项目资助
关键词 图像分类 超图学习 语义融合 Image Classification Hypergraph Learning Semantic Fusion
分类号 TP391.41 [自动化与计算机技术—计算机应用技术]
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参考文献16

  • 1Guillaumin M, Verbeek J, Schmid C. Muhimodal Semi-Supervised Learning for Image Classification//Proc of the IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA, 2010:902-909.
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同被引文献25

  • 1Mitsuru Kakimot, Chie Morita, Hiroshi Tsukimoto. Data mining from functional brain images[Z]. Proc. of MDM/ KDD2000 Workshop on Multimedia Data Mining, Boston, MA USA, 2000.
  • 2Osmar R.Zaiane, Jiawei Han, Ze NianLi, Sonny H.ehee, Jenny Ychiang. MultimediaMiner: A System Prototype for MultiMedia DataMining. In Proc. 1998 ACM-SIGMOD Cong, On management of data, June 1995.
  • 3WeiJiang, GuihuaEr. Similarity-based online feature selec- tion in content-based image retrieval [ J]. IEEE Transac- tions on Image Processing,2006,15(3) :702- 712.
  • 4Li, J Wang, J Z. Real-time computerized annotation of pictures [A]. In Proceedings of the ACM International Conference on Multimedia[C]. October 23 - 27, 2006, Santa Barbara, California, USA, 2006. 911 - 920.
  • 5DENG J,DONG W,SOCHER R,et al. ImageNet: A large - scale hier-archical image database [ EB/OL ] . [ 2014 - 05 - 15 ] . http://www. researchgate. net/publication/221361415_ImageNet_ A_large -soale_hierarchical_image_database.
  • 6XIAO J,HAYS J,EHINGER K,et al. Sun database : Large - scale scenerecognition from abbey to zoo [ EB/OL ] . [ 2014 - 05 - 15 ] . http://www. researchgate. net/publication/221362554_SUN _database _Large -scale 一scene一recognition一from_abbey_to一zoo.
  • 7BENGIO S, WESTON J, GRANGIER D. Label embedding trees forlarge multi - class tasks [ EB/OL ] . [ 2014 - 05 - 15 ] . http://www. researchgate. net/publication/221618910 _ Label _ Embedding __Trees_for__Large_Multi - Class_Tasks? ev = auth一pub.
  • 8GAO T, KOLLER D. Discriminative learning of relaxed hierarchy forlarge — scale visual recognition [ EB/OL] ? [ 2014 — 05 - 15]. http://www. sciweavers. org/publications/discriminative - learning ~ relaxed -hierarchy - large - scale - visual - recognition.
  • 9II L Multiclass boosting with repartitioning[EB/OL]. [2014 -05 -15].http://www. researchgate. net/publicadon/221345684_Multiclass_boosting_with_repartitioning.
  • 10ALLWEIN E L’SCHAPIRE R E,SINGER Y. Reducing multiclass tobinary : a unifying approach for margin classifiers [ J ]. J. Mach. Learn.Res. ,2011,2(1) :113 -141.

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模式识别与人工智能
2014年 第2期

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