基于优化采样模式的紧凑而快速的二进制描述子

惠国保; 李东波; 童一飞

doi:10.3724/SP.J.1004.2014.00255

基于优化采样模式的紧凑而快速的二进制描述子

doi: 10.3724/SP.J.1004.2014.00255

1.
南京理工大学机械工程学院南京 210094

基金项目:

国家自然科学基金（61104171）资助

详细信息

作者简介:
李东波南京理工大学机械工程学院教授.主要研究方向为先进制造技术. E-mail：db_calla@yahoo.com.cn

计量
- 文章访问数: 1348
- HTML全文浏览量: 62
- PDF下载量: 944
- 被引次数: 0
出版历程
- 收稿日期: 2013-05-28
- 修回日期: 2013-09-18
- 刊出日期: 2014-02-20

A Binary Descriptor Based on Both Optimized Sampling Pattern and Image Sub-patches

1.
School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094

Funds:

Supported by National Natural Science Foundation of China (61104171)

摘要

摘要: 进一步挖掘图像补丁特征信息，提出了一种鲁棒性更高的二进制描述子. 针对传统的二进制描述子对旋转和视角变化鲁棒性差的问题，本文通过优化采样模式和分解图像补丁对其改进. 首先，通过对最新提出的采样模式特点的分析、测试，发现采样点密度和平滑重叠度对产生的描述子独特性有重要影响；据此，调整这两个影响因子，设计出一种优化的采样模式. 其次，利用像素点灰度值排序方法分解图像补丁，产生多个对应不同灰度段的亚补丁. 最后，将优化的采样模式映射到亚补丁上，随机提取样本点进行灰度值比较测试.所得到的二进制描述子不仅包含了补丁像素的灰度比较信息，而且包含了灰度排序信息. 通过对比实验看到本文的二进制描述子对特征识别匹配效果最好.本文的特征描述方法可应用于实时性要求高、内存紧凑的高质量目标识别.
- 关键点 /
- 图像补丁 /
- 二进制描述子 /
- 采样模式 /
- 灰度排序 /
- 亚补丁
Abstract: This paper proposes a more robust binary descriptor through further excavating feature information of image patch. Conventional binary descriptors such as binary robust independent elementary features (BRIEF) are not robust to rotation and viewpoint invariance, which is improved from two aspects in this paper. Firstly, an optimized sampling pattern is presented by tuning the density of sampling points and the overlapping size of receptive fields. Secondly, all pixels in the patch are classified according to their intensity order, so that the patch is decomposed into several sub-patches. Then, random tests on each sub-patch mapped with the optimized sampling pattern are repeatedly taken, and each test result is concatenated to form a distinct binary string of the sub-patch. The proposed descriptor encodes not only intensity-comparison information but also information about relative relationship of intensities. As a result, results based on experiments of performance evaluation have shown that the proposed binary descriptor outperforms the-state-of-the-art binary descriptors.
- Keypoint /
- image patch /
- binary descriptor /
- sampling pattern /
- intensity order /
- sub-patch

HTML全文

参考文献(27)

[1]	Mikolajczyk K, Schmid C. A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2005, 27(10): 1615-1630
[2]	Lowe D G. Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision, 2004, 60(2): 91-110
[3]	Bay H, Ess A, Tuytelaars T, van Gool L. SURF: speeded up robust features. Computer Vision and Image Understanding, 2008, 110(3): 346-359
[4]	Ke Y, Sukthankar R. PCA-SIFT: a more distinctive representation for local image descriptors. In: Proceedings of the 2004 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. Washington, D.C., USA: IEEE, 2004. Ⅱ-506-Ⅱ-513
[5]	Tola E, Lepetit V, Fua P. Daisy: an efficient dense descriptor applied to wide-baseline stereo. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(5): 815-830
[6]	Fan B, Wu F C, Hu Z Y. Aggregating gradient distributions into intensity orders: a novel local image descriptor. In: Proceedings of the 2011 IEEE Conference on Computer Vision and Pattern Recognition. Rhode Island, Providence, USA: IEEE, 2011. 2377-2384
[7]	Torralba A, Fergus R, Weiss Y. Small codes and large image databases for recognition. In: Proceedings of the 2008 IEEE Conference on Computer Vision and Pattern Recognition. Anchorage, Alaska, USA: IEEE, 2008. 1-8
[8]	Jégou H, Douze M, Schmid C. Improving bag-of-features for large scale image search. International Journal of Computer Vision, 2010, 87(3): 316-336
[9]	Salakhutdinov R, Hinton G. Learning a nonlinear embedding by preserving class neighbourhood structure. In: Proceedings of the 2007 International Conference on Artificial Intelligence and Statistics. Monte Carlo Resort, Las Vegas, Nevada, USA: IEEE, 2007. 412-419
[10]	Ozuysal M, Calonder M, Lepetit V, Fua P. Fast keypoint recognition using random ferns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2010, 32(3): 448-461
[11]	Lepetit V, Fua P. Keypoint recognition using randomized trees. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2006, 28(9): 1465-1479
[12]	Calonder M, Lepetit V, Ozuysal M, Trzcinski T, Strecha C, Fua P. BRIEF: computing a local binary descriptor very fast. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(7): 1281-1298
[13]	Leutenegger S, Chli M, Siegwart R. BRISK: binary robust invariant scalable keypoints. In: Proceedings of the 2011 International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2548-2555
[14]	Rublee E, Rabaud V, Konolidge K, Bradski G. ORB: an efficient alternative to SIFT or SURF. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 2564-2571
[15]	Alahi A, Ortiz R, Vandergheynst P. FREAK: fast retina keypoint. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition. Rhode Island, Providence, USA: IEEE, 2012. 2069-2076
[16]	Trzcinski T, Lepetit V. Efficient discriminative projections for compact binary descriptors. In: Proceedings of the 12th European Conference on Computer Vision. Firenze, Italy: IEEE, 2012. 228-242
[17]	Strecha C, Bronstein A, Bronstein M, Fua P. LDAHash: improved matching with smaller descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 34(1): 66-78
[18]	Trzcinski T, Christoudias M, Fua P, Lepetit V. Boosting binary keypoint descriptors. In: Proceedings of the 2013 IEEE Conference on Computer Vision and Pattern Recognition. Washington, D.C., USA: IEEE, 2013. 2874-2881
[19]	Wang Z H, Fan B, Wu F C. Local intensity order pattern for feature description. In: Proceedings of the 2011 IEEE International Conference on Computer Vision. Barcelona, Spain: IEEE, 2011. 603-610
[20]	Liu Ping-Ping, Zhao Hong-Wei, Zang Xue-Bai, Dai Jin-Bo. A fast local feature description algorithm. Acta Automatica Sinica, 2010, 36(1): 40-45(刘萍萍, 赵宏伟, 臧雪柏, 戴金波. 一种快速局部特征描述算法. 自动化学报, 2010, 36(1): 40-45)
[21]	Tang Yong-He, Lu Huan-Zhang, Hu Mou-Fa. Local feature description algorithm based on Laplacian. Optics and Precision Engineering, 2011, 19(12): 2999-3006(唐永鹤, 卢焕章, 胡谋法. 基于Laplacian的局部特征描述算法. 光学精密工程, 2011, 19(12): 2999-3006)
[22]	Ge Juan, Cao Wei-Guo, Zhou Wei, Gong Ming, Liu Liu, Li Hua. A local feature descriptor under color affine transformation. Journal of Computer-Aided Design & Computer Graphics, 2013, 25(1): 26-33(葛娟, 曹伟国, 周炜, 公明, 刘浏, 李华. 一种颜色仿射变换下的局部特征描述子. 计算机辅助设计与图形学学报, 2013, 25(1): 26-33)
[23]	Song Ke-Chen, Yan Yun-Hui, Chen Wen-Hui, Zhang Xu. Research and perspective on local binary pattern. Acta Automatica Sinica, 2013, 39(6): 730-744(宋克臣, 颜云辉, 陈文辉, 张旭. 局部二值模式方法研究与展望. 自动化学报, 2013, 39(6): 730-744)
[24]	Mair E, Hager G D, Burschka D, Suppa M, Hirzinger G. Adaptive and generic corner detection based on the accelerated segment test. In: Proceedings of the 11th European Conference on Computer Vision. Berlin, Heidelberg: Springer-Verlag, 2010. 183-196
[25]	Cao Ying, Miao Qi-Guang, Liu Jia-Chen, Gao Lin. Advance and prospects of AdaBoost algorithm. Acta Automatica Sinica, 2013, 39(6): 745-758(曹莹, 苗启广, 刘家辰, 高琳. AdaBoost算法研究进展与展望. 自动化学报, 2013, 39(6): 745-758)
[26]	Brown M, Hua G, Winder S. Discriminative learning of local image descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(1): 43-57
[27]	Cai H P, Mikolajczyk K, Matas J. Learning linear discriminant projections for dimensionality reduction of image descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2011, 33(2): 338-352