期刊文献+

面向叶子图像的植物归类的特征序列描述方法 被引量:2

Method of signatures description of leaves images for plant categorization
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摘要 针对叶子图像的植物数据库的归类系统,提出了一种新的基于高斯混合模型特征函数的图像特征序列描述方法。定义了图像的高斯混合模型、特征函数及其性质,用自适应的方法把图像分解为K个模型,并在每个分量模型和混合模型上定义由频谱、相位角和功率谱组成的局部特征序列和全局特征序列。在中国科学院智能计算所的叶子图像数据集leaves(ICL)上进行了K-means归类实验,结果表明该图像描述方法比LBP局部综合特征和高斯混合密度函数有更好的归类结果。 For plant dataset categorization by leaf images,this paper proposed a new image signature method based on Gaussian mixture models(GMMs) and characteristic function.Firstly,it defined the GMMs for image,characteristic function and its property.Secondly,the image was divided into K models self-adaptively.Finally,it defined the local and global image signatures with spectrum,phase-angle and power spectrum of feature functions on each component model and mixture models respectively.And it developed a K-means categorization system to testify the effectiveness of the proposed method on the leaves(ICL) from intelligent computing laboratory of chinese academy of sciences.Experiment results show that the:proposed signatures can achieve better result than the classical LBP local combining features and GMMs density function.
出处 《计算机应用研究》 CSCD 北大核心 2012年第12期4740-4742,4746,共4页 Application Research of Computers
基金 江苏省高校自然科学基金资助项目(10KJB520004) 常熟理工学院校级项目(CITJGGN201117)
关键词 局部特征 全局特征 图像归类 混合模型 local feature global feature image categorization mixture model
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参考文献19

  • 1张善文,王献峰.基于加权局部线性嵌入的植物叶片图像识别方法[J].农业工程学报,2011,27(12):141-145. 被引量:20
  • 2ZHANG Shah-wen, LEI Ying-ke. Modified locally linear discriminant embedding for plant leaf recognition [ J ]. Neurocomputing, 2011,74 ( 14-15) :2284-2290.
  • 3LI J, ALLINSON N M. A comprehensive review of current local features for computer vision [ J ]. Nourocomputing, 2008,71 ( 10- 12 ) : 1771-1787.
  • 4SCHMID C, MOHR R. Local gray value invariants for image retrieval [ J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 1997,19(5) : 530-535.
  • 5OJALA T, PIETIKAINEN M, MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns [J]. IEEE Trans on Pattern Analysis and Machine Intelligence, 2002,24(7):971-987.
  • 6LOWED G. Distinctive image features from scale-invariant keypoints [ J ]. International Journal of Computer Vision, 2004,60 ( 2 ) : 91 - 110.
  • 7QIAN Xue-ming, HUA Xian-sheng, CHEN Ping. PLBP: an effective local binary patterns texture descriptor with pyramid representation [ J]. Pattern Recognition ,2011,44 ( 10-11 ) :2502-2515.
  • 8NANNI L, BRAHNAM S, LUMINIA. Combining different local binary pattern variants to boost performance [ J ]. Expert Systems with Applications, 2011,38 ( 5 ) : 6209 - 6216.
  • 9ZHANG B, SHAN S, CHEN X, et al. Histogram of Gabor phase patterns (HGPP) :a novel object representation approach for face recognition[J]. IEEE Trans on Image Processing, 2007,16 ( 1 ) : 57- 68.
  • 10WANG Xiao-feng, HUANG De-shuang, DU Ji-xiang, et al. Classification of plant leaf images with complicated background [ J]. Applied Mathematics and Computation,2008,205 ( 2 ) : 916 - 926.

二级参考文献18

  • 1王晓峰,黄德双,杜吉祥,张国军.叶片图像特征提取与识别技术的研究[J].计算机工程与应用,2006,42(3):190-193. 被引量:114
  • 2Du Jixiang, Huang D S, Wang Xiaofeng, et al. Shape recognition based on radial basis probabilistic neural network and application to plant species identification[J]. Lecture Notes in Computer Science, Springer-Verlag, 2005, 3497: 281 -285.
  • 3Gu Xiao, Du Jixiang. Leaf recognition based on the skeleton segmentation[J]. Lecture Notes in Computer Science, Springer-Verlag, 2005, 3644: 253-262.
  • 4Li Y F, Zhu Q S, Cao Y K, et al, A leaf vein extraction method based on snakes technique[C]. Proceedings of IEEE International Conference on Neural Networks and Brain,2005,885-888.
  • 5Camargo Neto, J., Meyer, G.E., Jones, D.D., Samal, A.K. Plant species identification using Elliptic Fourier leaf shape analysis[J]. Computers and Electronics in Agriculture, 2006, 50(2): 121- 134.
  • 6Bruno O M, Plotze R O, Falvo M, et al. Fractal dimension applied to plant identification[J]. Inform. Sci, 2008, 178(12): 2722-2733.
  • 7Jolliffe I T. Principal Component Analysis[M]. Berlin: Springer, 1989.
  • 8Duda R, Hart P E, Stork D G. Pattern Classification, second edition[M]. Hoboken: John Wiley and Sons, 2001.
  • 9Belhumeur P N, Hepanha J P, David J Kriegman. Eigenfaces vs. fisherfaces: recognition using class specific linear projection[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1997, 19(7): 711 -720.
  • 10He X F, Niyogi P. Locality preserving projections[C]// Proceedings of Advances In Neural Information Processing Systems. Cambridge, MIT Press, 2004:153- 160.

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同被引文献24

  • 1韩忠伟,李晨,Florian Schmidt.基于支持向量机和智能移动设备的多类树叶分类系统[J].生物技术世界,2013,10(3):173-173. 被引量:1
  • 2Lee W, Slaughter D. Recognition of partially occluded plant leaves using a modified watershed algorithm[ J ]. American Society or Ag- ricultural Engineers ,2004,47(4 ) : 1269-1280.
  • 3Ling Haibin, Jacobs D. Shape classification using the inner-distance [ J]. Pattern Analysis and Machine Intelligence, 2007,29 (2): 286 - 299.
  • 4Soederkvist O. Computer vision classification of leaves from Swedish trees[ D]. Linkoping:Linkoping University ,2001.
  • 5Pompanomchai C,Kuakiatngam C. Leaf and flower recognition system (e-Botanist) [ J]. International Journal of Engineering and Tech- nology,20l 1,3(4) : 347-351.
  • 6Kazakova N, Margala M, Durdle N. Sobel edge detection processor for a real-time volume rendering system [ C ]//Proc of International Sym- posium on Circuits and Systems. 2004:23-26.
  • 7Chen Li,Shirahama K,Czajkowska J,et al. A muhi-stage approach for automatic classification of environmental microorganisms[ C ~//Proc ~ff International Conference on Image Processing, Computer Vision, and Pattern Recognition. Las Vegas : CSREA Press,2013:364- 370.
  • 8Frigui H, Gader P. Detection and discrimination of land mines in ground-penetrating radar based on edge histogram descriptors and a possibilistic K-nearest neighbor classifier [ J ]. Fuzzy Systems, 2011,17( 1 ) : 185-199.
  • 9Belongie S, Malik J, Puzicha J. Shape matching and object recognition using shape contexts [ J ]. Pattern Analysis and Machine Intelli- gence, 2002,24 ( 4 ) : 509 - 522.
  • 10Zhang Dengsheng,Lu Guojun. A comparative study of curvature scale space and Fourier descriptors for shape-based image retrieval [ J ]. Journal of Visual Communication and Image Representation, 2003,14( 1 ) :39-57.

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