基于半色调图像的邻域相似性描述子方法

doi:10.6040/j.issn.1672-3961.1.2015.092

摘要/Abstract

摘要： 局部二值化模式是对局部信息进行特征提取,适合解决纹理特征不明显的图像特征提取问题,但这种方法存在特征维数高的问题。针对此问题,提出一种适用于半色调图像特征提取的邻域相似性描述子方法。该方法将8个邻域分别与中心像素值进行比较运算,然后求这8个运算值间的相似性指标。相似性指标的统计量进行归一化后作为图像的纹理特征向量。试验中,采用BP神经网络对提取图像特征进行分类试验。试验结果表明,在计算复杂度、识别精度等方面,本研究提出的方法优于单纯使用局部二值化模式算法。

关键词: 中心像素, 特征提取, 邻域相似性描述子, 局部二值化模式, 半色调图像

Abstract: Local binary pattern was obtained by extracting local features, which was appropriate for unobvious textural features but suffered high feature dimension problem. To solve this problem, the neighborhood similarity descriptor method for halftone image feature extraction was proposed. First, the center pixel was compared with its eight neighborhood pixels. Second, the similarity index was computed for those pixel pairs. The similarity index was taken as textural feature vector after normalizing the statistics. Finally, BP neural network was adopted to classify the extracted image features in experiment. Experimental results showed that the proposed method was better than the local binary pattern algorithm in the computational complexity and recognition accuracy.

Key words: local binary pattern, halftone image, feature extraction, neighborhood similarity descriptor, center pixel

中图分类号:

TP391

钟智彦,文志强, 张潇云,叶德刚. 基于半色调图像的邻域相似性描述子方法[J]. 山东大学学报(工学版), 2016, 46(3): 58-64.

ZHONG Zhiyan, WEN Zhiqiang, ZHANG Xiaoyun, YE Degang. Neighborhood similarity descriptor used in halftone image[J]. JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE), 2016, 46(3): 58-64.

参考文献

[1] 文志强,胡永祥,朱文球. 基于统计量模板的半色调图像特征提取与分类[J]. 计算机科学,2013, 40(12):94-112. WEN Zhiqiang, HU Yongxiang, ZHU Wenqiu. Feature extraction and classification of halftone image based on statistics template[J].Compute Science, 2013, 40(12):94-112.
[2] 孔月萍,曾平.图像逆半调及其质量评价技术研究[D].西安:西安电子科技大学,2008. KONG Yueping, ZENG Ping. A study of inverse halftoning and quality assessment schemes[D]. Xi'an:Xidian University, 2008.
[3] ULICHNEY R A. Dithering with blue noise[J]. Proceedings of the IEEE,1988, 76(1):56-79.
[4] STEVEBSON R. Inverse halftoning via MAP estimation[J].IEEE Transaction on Image Processing, 1997, 6(4):574-583.
[5] KNUTH D E. Digital halftones by dot diffusion[J]. ACM Transactions on Graphics(TOG), 1987, 6(4):245-273.
[6] MESE M, VAIDYANATHAN P P. Optimized halftoning using dot diffusion and methods for inverse halftoning[J]. Image Processing, IEEE Transactions on, 2000, 9(4):691-709.
[7] MESE M, VAIDYANATHAN P P. Recent advances in digital halftoning and inverse halftoning methods[J]. IEEE Trans. on Ciruits and Systems: Fundamental Theory and Applications, 2002, 49(6):790-805.
[8] SELDOWITZ M A, ALLEBACH J P, SWEENEY D E. Synthesis of digital holograms by direct binary search[J]. Applid Optics, 1987, 26(14):2788-2798.
[9] LIU Y F, GUO J M, LEE J D.Inverse halftoning based on the Bayesian theorem[J]. IEEE Trans on Image Processing, 2011, 20(4):1077-1084.
[10] CHANG P C,YU C S.Neural net classification and LMS reconstruction to halftone images[C] //Proceedings of SPIE-The International Society for Optical Engineering. San Jose, USA: SPIE, 1997:592-602.
[11] 刘丽,匡纲要.图像纹理特征提取方法综述[J].中国图象图形学报,2009,14(4):622-635. LIU Li, KUANG Gangyao. Overview of image texture feature extraction methods[J]. Journal of Image and Graphics, 2009, 14(4):622-635.
[12] RIDDER D, LOOG M, REINDERS M. Local fisher embedding[C] //Proceedings of the 17th International Conference on Pattern Recognition. New York, USA: IEEE, 2004:295-298.
[13] 文志强,朱文球,胡永祥.半调图像的分类方法[J].山东大学学报(工学版), 2013, 43(4):7-12. WEN Zhiqiang, ZHU Wenqiu, HU Yongxiang. A classification method of halftone image[J]. Journal of Shandong University(Engineering Science), 2013, 43(4):7-12.
[14] 孔月萍,曾平,张跃鹏.一种半调图像分类识别算法[J]. 西安电子科技大学学报(自然科学版),2011,38(5):62-69. KONG Yueping, ZENG Ping, ZHANG Yuepeng. Classification and recognition algorithm for the halftone image[J]. Journal of Xidian University(Nature Science), 2011, 38(5):62-69.
[15] WEN Zhiqiang, HU Yongxiang, ZHU Wenqiu. A novel classification method of halftone image via statistics matrices[J]. IEEE Transaction on Image Processing, 2014, 23(11):4724-4736.
[16] 梁永峰,黄进.一种改进的误差扩散算法[D].西安:西安电子科技大学, 2013. LIANG Yongfeng, HUANG Jin. An improve error diffusion algorithm[D]. Xi'an:Xidian University, 2013.
[17] 姚莉.数字半调技术及其评价方法研究[J].计算机工程与应用,2010,46(3):4-8. YAO Li. Review on digital halftoning and quality assessment schemes[J]. Computer Engineering and Applications, 2010, 46(3):4-8.
[18] OJALA T, PIETIKAINEN M, HARWOOD D. A comparative study of texture measures with classification based on featured distributions[J]. Pattern Recognition, 1996, 29(1):51-59.
[19] 徐少平,刘小平,李春泉,等. 基于LBP值对空间统计特征的纹理描述符[J]. 模式识别与人工智能,2013, 26(8):669-776. XU Shaoping, LIU Xiaoping, LI Chunquan, et al. Texture descriptor based on spatial feature of local binary pattern code pair[J]. Pattern Recognition and Artificial Intelligence, 2013, 26(8):669-776.
[20] CHUN H, CHANG H, LIU T. Local discriminant embedding and its variants[C] //Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. New York, USA: IEEE, 2005:846-853.
[21] FREITAS P G, FARIAS M C Q, DEARAUJO A P F. Fast inverse halftoning algorithm for ordered dithered images[C] //Graphics, Patterns and Images(Sibgrapi), 2011 24th SIBGRAPI Conference on. Alagoas, Brazil: IEEE, 2011:250-257.
[22] HUANG Di, SHAN Caifeng, ARDABILIAN Metal. Local binary patterns and its application to facial image analysis: a survey[J]. IEEE transaction on Systems, Man and Cybernetics, 2011, 41(6):765-781.
[23] REN J, JIANG X, YUAN J. Noise-resistant local binary pattern with an embedded error-correction mechanism[J]. IEEE transaction on Image Processing, 2013, 22(10):4049-4060.
[24] BAQAI F A, ALLEBACH J P. Halftoning via direct binary search using analytical and stochastic printer models[J]. Image Processing, IEEE Transactions on, 2003, 12(1):1-15.
[25] LIAO S, ZHAO G, KELLOKUMPU V, et al. Modeling pixel process with scale invariant local patterns for background subtraction in complex scenes[C] //Proceedings of the 23th IEEE Conference on Computer Vision and Pattern Recognition. San Francisco, USA:IEEE, 2010:1301-1306.
[26] TAN X,TRIGGS B. Enhanced local texture feature sets for face recognition under difficult lighting conditions[J]. IEEE transaction on Image Processing, 2010, 19(6):1635-1650.

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