基于全局与局部形状特征融合的形状识别算法

doi:10.13229/j.cnki.jdxbgxb201605036

摘要/Abstract

摘要： 经典的全局形状识别算法虽然高效,但在处理形变方面存在不足。局部形状识别算法拥有良好的检索率,但在辨别力方面的效果却有待提高。为解决上述问题,本文提出一种基于特征点分类的融合框架,该框架不仅融合了全局与局部算法的优势,还弥补了二者的不足。一些经典的形状识别算法采用提取特征点的方式来构建形状特征直方图,本文在此基础上,将提取到的特征点进一步分类,针对不同类别的特征点集合采用不同的形状识别算法进行描述,并将匹配结果进行融合,充分发挥了全局与局部算法的优势。实验结果表明,本文提出的框架能够有效结合不同算法实现形状的识别并获得更好的效果。

关键词: 计算机应用, 形状识别, 特征点, 形状特征直方图, 特征融合

Abstract: Although the classical global shape recognition algorithm is efficient, it is not good enough to deal with the deformation. The local shape recognition algorithm has a good retrieval rate, however, the discriminability still needs improvement. To solve these problems, a fusion framework based on the classification of characteristic points is proposed, which not only takes the advantages of global and local shape recognition algorithms, but also makes up for the lacks of the two algorithms. Some classic shape recognition algorithms build the shape feature histogram by extracting the characteristic points. Based on this, these points are further classified that different shape recognition algorithms are applied to different kinds of points. The matching results are then fused to make full used of the advantages of the global and local shape feature descriptors. Experiment results show that the proposed framework can effectively combine different algorithms to achieve the shape recognition and get better results.

Key words: computer application, shape recognition, characteristic point, shape feature histogram, feature fusion

中图分类号:

TP391

王生生, 郭湑, 张家晨, 王光耀, 赵欣. 基于全局与局部形状特征融合的形状识别算法[J]. 吉林大学学报(工学版), 2016, 46(5): 1627-1632.

WANG Sheng-sheng, GUO Xu, ZHANG Jia-chen, WANG Guang-yao, ZHAO Xin. Shape recognition algorithm based on fusion of global and local properties[J]. 吉林大学学报(工学版), 2016, 46(5): 1627-1632.

参考文献

[1] Zhang D S,Lu G J. Review of shape representation and description techniques[J]. Pattern Recognition, 2004,37(1): 1-19.
[2] 赵宏伟, 陈霄, 石景海, 等. 综合颜色和形状特征的交通标志图像检索算法[J]. 吉林大学学报:工学版, 2013, 43(增刊1): 128-132.
Zhao Hong-wei, Chen Xiao, Shi Jing-hai, et al. Traffic sign image retrieval algorithm using integrated color and shape features[J]. Journal of Jilin University(Engineering and Technology Edition), 2013, 43(Sup.1): 128-132.
[3] 李平,魏仲慧,何昕,等.采用多形状特征融合的多视点目标识别[J].光学精密工程, 2014,22(12):3368-3376.
Li Ping, Wei Zhong-hui, He Xin, et al. Object recognition based on shape feature fusion under multi-views[J]. Optics and Precision Engineering, 2014,22(12):3368-3376.
[4] 汪华章,何小海,宰文姣.基于局部和全局特征融合的图像检索[J].光学精密工程,2008,16(6):1098-1104.
Wang Hua-zhang, He Xiao-hai, Zai Wen-jiao. Image retrieval based on combining local and global features[J]. Optics and Precision Engineering,2008,16(6):1098-1104.
[5] Daliri M R, Torre V. Robust symbolic representation for shape recognition and retrieval[J]. Pattern Recognition, 2008, 41(5): 1782-1798.
[6] Velasco-Forero S, Angulo J. Statistical shape modeling using morphological representations[C]∥International Conference on Pattern Recognition, Istanbul, 2010:3537-3540.
[7] Wang B, Shen W, Liu W Y, et al. Shape classification using tree-unions[C]∥International Conference on Pattern Recognition, Istanbul, 2010:983-986.
[8] Escalera S, Fornés A, Pujol O, et al.Circular blurred shape model for multiclass symbol recognition[J]. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, 2011, 41(2): 497-506.
[9] Belongie S J, Malik J, Puzicha J. Shape matching and object recognition using shape contexts[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2002,24(4):509-522.
[10] Ling H B, Jacobs D W. Shape classification using the inner-distance[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2007, 29(2): 286-299.
[11] Wang J W, Bai X, You X, et al. Shape matching and classification using height functions[J]. Pattern Recognition Letters, 2012,33(2):134-143.
[12] Ling H B, Yang X W, Latecki L J. Balancing deformability and discriminability for shape matching[C]∥11th European Conference on Computer Vision,Heraklion, Greece, 2010,6313: 411-424.
[13] Bai X, Wang B, Yao C, et al. Co-transduction for shape retrieval[J]. IEEE Transactions on Image Processing, 2012,21(5): 2747-2757.
[14] 柯洪昌, 孙宏彬,. 图像序列的显著性目标区域检测方法[J]. 中国光学, 2015,8(5): 768-774
Ke Hong-chang, Sun Hong-bin. A saliency target area detection method of image sequence[J]. Chinese Optics, 2015,8(5): 768-774
[15] Escalera S, Fornés A, Pujol O, et al. Blurred shape model for binary and grey-level symbol recognition[J]. Pattern Recognition Letters, 2009,30(15):1424-1433.
[16] Lopes O, Reyes M, Escalera S, et al. Spherical blurred shape model for 3-D object and pose recognition: quantitative analysis and HCI applications in smart environments[J]. IEEE Transactions on Cybernetics, 2014,44(12):2379-2390.
[17] Latecki L J, Lakamper R, Eckhardt T. Shape descriptors for non-rigid shapes with a single closed contour[C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Hilton Head Island, USA: IEEE, 2000:424-429.
[18] Sebastian T B, Klein P N, Kimia B B. Recognition of shapes by editing their shock graphs[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2004, 26(5): 550-571.

相关文章 15

[1]	刘富,宗宇轩,康冰,张益萌,林彩霞,赵宏伟. 基于优化纹理特征的手背静脉识别系统[J]. 吉林大学学报(工学版), 2018, 48(6): 1844-1850.
[2]	王利民,刘洋,孙铭会,李美慧. 基于Markov blanket的无约束型K阶贝叶斯集成分类模型[J]. 吉林大学学报(工学版), 2018, 48(6): 1851-1858.
[3]	金顺福,王宝帅,郝闪闪,贾晓光,霍占强. 基于备用虚拟机同步休眠的云数据中心节能策略及性能[J]. 吉林大学学报(工学版), 2018, 48(6): 1859-1866.
[4]	赵东,孙明玉,朱金龙,于繁华,刘光洁,陈慧灵. 结合粒子群和单纯形的改进飞蛾优化算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1867-1872.
[5]	刘恩泽,吴文福. 基于机器视觉的农作物表面多特征决策融合病变判断算法[J]. 吉林大学学报(工学版), 2018, 48(6): 1873-1878.
[6]	欧阳丹彤, 范琪. 子句级别语境感知的开放信息抽取方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1563-1570.
[7]	刘富, 兰旭腾, 侯涛, 康冰, 刘云, 林彩霞. 基于优化k-mer频率的宏基因组聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1593-1599.
[8]	桂春, 黄旺星. 基于改进的标签传播算法的网络聚类方法[J]. 吉林大学学报(工学版), 2018, 48(5): 1600-1605.
[9]	刘元宁, 刘帅, 朱晓冬, 陈一浩, 郑少阁, 沈椿壮. 基于高斯拉普拉斯算子与自适应优化伽柏滤波的虹膜识别[J]. 吉林大学学报(工学版), 2018, 48(5): 1606-1613.
[10]	车翔玖, 王利, 郭晓新. 基于多尺度特征融合的边界检测算法[J]. 吉林大学学报(工学版), 2018, 48(5): 1621-1628.
[11]	赵宏伟, 刘宇琦, 董立岩, 王玉, 刘陪. 智能交通混合动态路径优化算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1214-1223.
[12]	黄辉, 冯西安, 魏燕, 许驰, 陈慧灵. 基于增强核极限学习机的专业选择智能系统[J]. 吉林大学学报(工学版), 2018, 48(4): 1224-1230.
[13]	傅文博, 张杰, 陈永乐. 物联网环境下抵抗路由欺骗攻击的网络拓扑发现算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1231-1236.
[14]	曹洁, 苏哲, 李晓旭. 基于Corr-LDA模型的图像标注方法[J]. 吉林大学学报(工学版), 2018, 48(4): 1237-1243.
[15]	侯永宏, 王利伟, 邢家明. 基于HTTP的动态自适应流媒体传输算法[J]. 吉林大学学报(工学版), 2018, 48(4): 1244-1253.

Metrics

Viewed

Full text

Abstract

Cited

Shared

Discussed