图像微观结构的二值化表示与目标识别应用

张东波; 陈治强; 易良玲; 许海霞

doi:10.11999/JEIT170513

图像微观结构的二值化表示与目标识别应用

doi: 10.11999/JEIT170513

基金项目:

国家自然科学基金(61602397)，湖南省自然科学基金(2017JJ2251, 2017JJ3315)，湖南省重点学科建设项目

计量
- 文章访问数: 1275
- HTML全文浏览量: 172
- PDF下载量: 316
- 被引次数: 0
出版历程
- 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: 该文提出一种新颖的基于二值图像微观结构模式(Binary Image Micorsructure Pattern, BIMP)表达和灰度图像微观结构二值模式(Gray Image Micorsruct Maximum Response Pattern, GIMMRP)编码方法。通过对图像33邻域结构进行二值编码，获得图像微观结构的描述，进而选取其中的重要执行模式子集和池化操作，实现整体图像的表示。为了检验算法的有效性，在ORL, YALE两个人脸公开数据集，MNIST, USPS两个手写数字公开数据集，以及非公开车标数据集上进行了测试，显示该方法具有很强的鉴别能力和鲁棒性，可以达到和超过很多最新算法的性能。
- 目标识别 /
- 微观结构 /
- 二值图像微观结构模式 /
- 灰度图像微观结构最大响应模式编码
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 /

HTML全文

参考文献(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.

施引文献

资源附件(0)

访问统计