圖像微觀結(jié)構(gòu)的二值化表示與目標(biāo)識(shí)別應(yīng)用

張東波; 陳治強(qiáng); 易良玲; 許海霞

doi:10.11999/JEIT170513

圖像微觀結(jié)構(gòu)的二值化表示與目標(biāo)識(shí)別應(yīng)用

doi: 10.11999/JEIT170513

基金項(xiàng)目:

國家自然科學(xué)基金(61602397)，湖南省自然科學(xué)基金(2017JJ2251, 2017JJ3315)，湖南省重點(diǎn)學(xué)科建設(shè)項(xiàng)目

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- 文章訪問數(shù): 1296
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- 被引次數(shù): 0
出版歷程
- 收稿日期: 2017-05-27
- 修回日期: 2017-10-19
- 刊出日期: 2018-03-19

Binarization Representation of Image Microstructure and the Application of Object Recognition

Funds:

The National Natural Science Foundation of China (61602397), The Natural Science Foundation of Hunan Province (2017JJ2251, 2017JJ3315), The Key Discipline Construction Project of Hunan Province

摘要

摘要: 該文提出一種新穎的基于二值圖像微觀結(jié)構(gòu)模式(Binary Image Micorsructure Pattern, BIMP)表達(dá)和灰度圖像微觀結(jié)構(gòu)二值模式(Gray Image Micorsruct Maximum Response Pattern, GIMMRP)編碼方法。通過對圖像33鄰域結(jié)構(gòu)進(jìn)行二值編碼，獲得圖像微觀結(jié)構(gòu)的描述，進(jìn)而選取其中的重要執(zhí)行模式子集和池化操作，實(shí)現(xiàn)整體圖像的表示。為了檢驗(yàn)算法的有效性，在ORL, YALE兩個(gè)人臉公開數(shù)據(jù)集，MNIST, USPS兩個(gè)手寫數(shù)字公開數(shù)據(jù)集，以及非公開車標(biāo)數(shù)據(jù)集上進(jìn)行了測試，顯示該方法具有很強(qiáng)的鑒別能力和魯棒性，可以達(dá)到和超過很多最新算法的性能。
- 目標(biāo)識(shí)別 /
- 微觀結(jié)構(gòu) /
- 二值圖像微觀結(jié)構(gòu)模式 /
- 灰度圖像微觀結(jié)構(gòu)最大響應(yīng)模式編碼
Abstract: A novel expression based on Binary Image Microstructure Pattern (BIMP) and Gray Image Micorstructure Maximum Response Pattern (GIMMRP) coding method is proposed. Through the binary coding of the 33 neighborhood structure of the image, the description of the microstructure of the image is obtained, and then selecting the important execution mode subset and the pooling operation to realize the representation of the whole image. In order to verify the effectiveness of the algorithm, experiments are carried out on the ORL, YALE two human face data set, MNIST, USPS two handwritten digital public data sets, as well as non-public vehicle standard data set. The results show the method has strong discriminative power and robustness and can achieve better performance than many of the latest algorithms.
- Object recognition;Micorstructure /
- Binary Image Micorsructure Pattern (BIMP) /
- Gray Image Micorsruct Maximum Response Pattern (GIMMRP) coding /

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參考文獻(xiàn)(24)

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.

LOWE D G. Distinctive image features from scale- invariantkeypoints[J]. International Journal of Computer Vision, 2004, 60(2): 91-110. doi: 10.1023/B:VISI.0000029664. 99615.94.

MIKOLAJCZYK K and SCHMID C. A performance evaluation of local descriptors[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2005, 27(10): 1615-1630. doi: 10.1109/TPAMI.2005.188.

ENGIN Tola, LEPETIT Vincent, and FUA Pascal. Daisy: An efficient dense descriptor applied to wide-baseline stereo[J]. IEEE Transactions on Pattern Analysis Machine Intelligence, 2010, 32(5): 815-830. doi: 10.1109/TPAMI.2009. 77.

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.

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.

OJALA T, PIETIKAINEN M, and MAENPAA T. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002, 24(7): 971-987. doi: 10.1109/TPAMI. 2002.1017623.

GUO Z H, ZHANG L, and ZHANG D. A completed modeling of local binary pattern operator for texture classification[J]. IEEE Transactions on Image Processing, 2010, 19(6): 1657-1663. doi: 10.1109/TIP.2010.2044957.

LIAO S, LAW M W K and CHUNG A C S. Dominant local binary patterns for texture classification[J]. IEEE Transactionson Image Processing, 2009, 18(5): 1107-1118. doi: 10.1109/TIP.2009.2015682.

MAENPAA T, OJALA T, and PIETIKAINEN M. Robust texture classification by subsets of local binary patterns[C]. Proceedings of the 2000 International Conference on Pattern Recognition, Barcelona, Brazil, 2000: 947-950. doi: 10.1109/ ICPR.2000.903698.

HAMDAN B and MOKHTAR K. Face recognition using Angular Radial Transform[J]. Journal of King Saud University-Computer and Information Sciences, 2016. doi: 10.1016/j.jksuci.2016.10.006.

ZHU N B, TANG T, and TANG S. 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.

ZHANG Y Y and ZHAO D. 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.

HUANG P and LAI Z H. Adaptive linear discriminant regression classification for face recognition[J]. Digital Signal Processing, 2016, 55: 78-84. doi: 10.1016/j.dsp.2016.05.001.

WANG S J and ZHOU C G. 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.

WANG G Q and SHI N F. Embedded manifold-based kernel fisher discriminant analysis for face recognition[J]. Neural Processing Letters, 2016, 43(1): 1-16. doi: 10.1007/s11063- 014-9398-x.

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.

SHAO H and CHEN S. 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.

DING S F and GUO L L. 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.

ZHOU Y and SUN S L. Manifold partition discriminant analysis[J]. IEEE Transactions on Cybernetics, 2017, 47(4): 830-840. doi: 10.1109/TCYB.2016.2529299.

WU T F and LIN C J. Probability estimates for multi-class classification by pairwise coupling[J]. Journal of Machine Learning Research, 2004, 5(4): 975-1005.

SCHMIDHUBER J, CIRES D, and MEIER U. Multi-column 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.

ZHANG Z M and LADICKY L. Learning anchor planes for Classification[C]. Advances in Neural Information Processing Systems, Granada, Spain, December, 2011: 1611-1619.

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.

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