多尺度局部结构主导二值模式学习图像表示
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摘要
通过零均值化的微观结构模式二值化(ZMPB)处理,该文提出一种立足于局部图像多尺度结构二值模式提取的图像表示方法。该方法能够表达图像中可能出现的各种具有视觉意义的重要模式结构,同时通过主导二值模式学习模型,可以获得适应于图像数据集的主导特征模式子集,在特征鲁棒性、鉴别力和表达能力上达到优异性能,同时可以有效降低特征编码的维度,提高算法的执行速度。实验结果表明该算法性能优异,具有很强的鉴别能力和鲁棒性,优于传统LBP和GIMMRP方法,和很多最新算法结果相比,也具有竞争优势。
By means of Zero-mean Microstructure Pattern Binarization(ZMPB), an image representation method based on image local microstructure binary pattern extraction is proposed. The method can express all the important patterns with visual meaning that may occur in the image. Moreover, through the dominant binary pattern learning model, the dominant feature pattern set adapted to the different data sets is obtained,which not noly achieves excellent ability in feature robustness, discriminative and representation, but also can greatly reduce the dimension of feature coding and improve the execution speed of the algorithm. The experimental results show that the proposed method has strong discriminative power and outperformes the traditional LBP and GIMMRP methods. Compared with many recent algorithms, the proposed method also presents a competitive advantage.
By means of Zero-mean Microstructure Pattern Binarization(ZMPB), an image representation method based on image local microstructure binary pattern extraction is proposed. The method can express all the important patterns with visual meaning that may occur in the image. Moreover, through the dominant binary pattern learning model, the dominant feature pattern set adapted to the different data sets is obtained,which not noly achieves excellent ability in feature robustness, discriminative and representation, but also can greatly reduce the dimension of feature coding and improve the execution speed of the algorithm. The experimental results show that the proposed method has strong discriminative power and outperformes the traditional LBP and GIMMRP methods. Compared with many recent algorithms, the proposed method also presents a competitive advantage.
引文
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[23]EBRAHIMZADEH R and JAMPOUR M.Efficient handwritten digit recognition based on histogram of oriented gradients and SVM[J].Annals of the Rheumatic Diseases,2014,104(9):10-13.doi:10.5120/18229-9167.
[2]BAY H and TUYTELAARS T.SURF:Speeded up robust features[J].Computer Vision&Image Understanding,2006,110(3):404-417.doi:10.1007/11744023_32.
[3]MIKOLAJCZYK K and SCHMID C.A performance evaluation of local descriptors[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,2005,27(10):1615-1630.doi:10.1109/TPAMI.2005.188.
[4]ENGIN T,LEPETIT V,and FUA P.Daisy:An efficient dense descriptor applied to wide-baseline stereo[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2010,32(5):815-830.doi:10.1109/TPAMI.2009.77.
[5]DALAL N and TRIGGS B.Histograms of oriented gradients for human detection[C].IEEE Computer Society Conference on Computer Vision&Pattern Recognition,San Francisco,USA,2005:886-893.doi:10.1109/CVPR.2005.177.
[6]OJALA T,VALKEALAHTI K,OJA E,et al.Texture discrimination with multidimensional distributions of signed gray-level differences[J].Pattern Recognition,2001,34(3):727-739.doi:10.1016/S0031-3203(00)00010-8.
[7]OJALA T,PIETIKAINEN M,and MAENPAA T.Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J].IEEETransactions on Pattern Analysis and Machine Intelligence,2002,24(7):971-987.doi:10.1109/TPAMI.2002.1017623.
[8]LIAO S,LAW M W K,and CHUNG A C S.Dominant local binary patterns for texture classification[J].IEEETransactionson Image Processing,2009,18(5):1107-1118.doi:10.1109/TIP.2009.2015682.
[9]张东波,陈治强,易良玲,等.图像微观结构的二值化表示与目标识别应用[J].电子与信息学报,2018,40(3):633-640.doi:10.11999/JEIT170513.ZHANG Dongbo,CHEN Zhiqiang,YI Liangling,et al.Binarization representation of image microstructure and the application of object recognition[J].Journal of Electronics&Information Technology,2018,40(3):633-640.doi:10.11999/JEIT170513.
[10]HAMDAN B and MOKHTAR K.Face recognition using Angular Radial Transform[J].Journal of King Saud University-Computer and Information Sciences,2016,30(2):141-151.doi:10.1016/j.jksuci.2016.10.006.
[11]ZHU Ningbo,TANG Ting,and TANG Shi.A sparse representation method based on kernel and virtual samples for face recognition[J].Optik-International Journal for Light and Electron Optics,2013,124(23):6236-6241.doi:10.1016/j.ijleo.2013.05.017.
[12]ZHANG Yuanyuan and ZHAO Dong.Adaptive convolutional neural network and its application in face recognition[J].Neural Processing Letters,2016,43(2):389-399.doi:10.1007/s11063-015-9420-y.
[13]HUANG Pu and LAI Zhihui.Adaptive linear discriminant regression classification for face recognition[J].Digital Signal Processing,2016,55:78-84.doi:10.1016/j.dsp.2016.05.001.
[14]WANG Sujing and ZHOU Chunguang.Face recognition using second-order discriminant tensor subspace analysis[J].Neurocomputing,2011,74(12/13):2142-2156.doi:10.1016/j.neucom.2011.01.024.
[15]WANG Guoqiang and SHI Nianfeng.Embedded manifoldbased kernel fisher discriminant analysis for face recognition[J].Neural Processing Letters,2016,43(1):1-16.doi:10.1007/s11063-014-9398-x.
[16]SINGH G and CHHABRA I.Integrating global zernike and local discriminative HOG features for face recognition[J].International Journal of Image&Graphics,2016,16(4):1650021-1650042.doi:10.1142/S0219467816500212.
[17]SHAO Hong and CHEN Shuang.Face recognition based on subset selection via metric learning on manifold[J].Frontiers of Information Technology&Electronic Engineering,2015,16(12):1046-1058.doi:10.1631/FITEE.1500085.
[18]DING Shifei and GUO Lili.Extreme learning machine with kernel model based on deep learning[J].Neural Computing&Applications,2017,28(8):1975-1984.doi:10.1007/s00521-015-2170-y.
[19]ZHOU Yang and SUN Shiliang.Manifold partition discriminant analysis[J].IEEE Transactions on Cybernetics,2017,47(4):830-840.doi:10.1109/TCYB.2016.2529299.
[20]WU Tingfang and LIN C J.Probability estimates for multiclass classification by pairwise coupling[J].Journal of Machine Learning Research,2004,5(4):975-1005.
[21]SCHMIDHUBER J,CIRES D,and MEIER U.Multicolumn deep neural networks for image classification[C].IEEE Conference on Computer Vision&Pattern Recognition,Rod Aprovendis,USA,2012:3642-3649.doi:10.1109/CVPR.2012.6248110.
[22]ZHANG Ziming and LADICKY L.Learning anchor planes for Classification[C].Advances in Neural Information Processing Systems,Granada,Spain,2011:1611-1619.
[23]EBRAHIMZADEH R and JAMPOUR M.Efficient handwritten digit recognition based on histogram of oriented gradients and SVM[J].Annals of the Rheumatic Diseases,2014,104(9):10-13.doi:10.5120/18229-9167.