Journal of Jilin University(Engineering and Technology Edition) ›› 2019, Vol. 49 ›› Issue (4): 1345-1356.doi: 10.13229/j.cnki.jdxbgxb20171157

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Extended contour⁃based fast affine invariant feature extracting

Feng⁃wen ZHAI1(),Jian⁃wu DANG1,2,Yang⁃ping WANG1,2,Jing JIN1,Wei⁃wei LUO1   

  1. 1. School of Electronic and Information Engineering, Lanzhou Jiao Tong University, Lanzhou 730070, China
    2. Gansu Provincial Engineering Research Center for Artificial Intelligence and Graphics & Image Processing, Lanzhou 730070, China
  • Received:2017-09-26 Online:2019-07-01 Published:2019-07-16

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Abstract:

To overcome the poor classification ability of the fast affine invariant feature extracting algorithm, an extended contour-based fast affine invariant feature extracting algorithm is developed. Firstly, the flaw in the proving and calculating procedures of the fast affine invariant feature extracting algorithm is proposed. Secondly, the definition of extended contour is proposed which is used to amend the fast affine invariant feature extraction algorithm. Thirdly, through simulation experiments, the parameter setting intervals in the algorithm are fixed and the numbers and powers of the component functions are determined. The simulation experiments are carried out to compared the proposed algorithm with the original fast affine invariant feature extracting algorithm and the multi-scale auto-convolution algorithm. The experimental results show that the proposed algorithm has better classification and recognition ability than the original fast affine invariant feature extracting algorithm and the multi-scale auto-convolution algorithm.

Key words: computer application, pattern recognition, affine invariant feature, extended contour, multi?scale auto convolution(MSA)

CLC Number: 

  • TP391.4

Fig.1

Integration area affine transformation, α=0.5"

Fig.2

Schematic diagram of extended contour trimming process (α=0.5)"

Fig.3

Extended contour images"

Fig.4

Sample images 1"

Fig.5

"

Fig.6

Sample images 2"

Fig.7

"

Fig.8

Sample images 3"

Fig.9

"

Fig.10

Comparison figures of separability measures of the interval setting test, results of COIL?100 database"

Fig.11

Comparison figures of separability measures of interval setting test, results of FISH database"

Fig.12

Comparison figures for component function numbers"

Fig.13

Comparison figures of separability measures of component number setting test"

Fig.14

Comparison figures for component function powers with COIL?100"

Fig.15

Comparison figures for component function powers with FISH"

Table 1

Comparison table of separability measures of the FAST_AFFINE,NEW_FAST_AFFINE and MSA"

J12 J13 J14 J23 J24 J34
T1 O 1.1032 2.7945 1.9064 0.4488 1.9761 8.0326
N 5.5070 7.2457 2.1370 2.8211 5.7799 7.0127
M 5.4017 8.3021 2.9541 2.2322 6.7721 10.5159
T2 O 5.0131 0.6587 0.9866 1.6523 9.3518 1.2623
N 5.3153 0.7623 1.3552 1.7161 8.2898 1.5486
M 4.6857 0.7613 1.6459 1.2520 5.2240 1.5440
T3 O 0.5680 1.2638 0.8324 0.8459 0.3213 0.6597
N 0.6110 2.2677 1.8270 0.9857 0.3839 0.4931
M 0.8256 2.1455 2.1840 0.2496 0.2407 0.5085
T4 O 0.8703 0.7001 0.4781 1.3752 0.877 0.2941
N 1.1560 3.5281 1.6704 1.7401 0.4597 1.1943
M 1.266 3.5481 1.8885 1.4718 0.2786 1.4369
T5 O 0.3155 0.3709 0.2800 0.4098 0.2011 0.3410
N 0.6723 0.6052 0.1345 1.4938 0.7477 0.7438
M 0.9683 0.7693 0.2938 1.6291 0.6796 0.9896
T6 O 1.2256 0.9845 0.5956 1.4401 0.2489 0.9891
N 1.8734 1.7978 1.1511 3.3365 1.4667 3.0824
M 2.1548 1.5891 1.3045 3.3482 1.6935 2.9658
T7 O 1.0424 1.7442 1.1964 1.5861 0.4114 1.6633
N 0.6220 1.2677 0.4870 2.8803 0.7553 2.9218
M 0.8511 0.8024 0.9422 2.5131 0.3957 3.4842
T8 O 9.4425 3.3529 1.9051 1.8982 1.5830 0.8370
N 3.1828 11.2549 4.0737 7.2851 2.2636 2.8422
M 6.7318 19.4526 4.5319 10.4487 1.5937 3.5344
T9 O 6.2368 0.5544 0.2710 0.9613 4.8863 0.4478
N 4.7109 1.2118 0.6017 0.4666 1.9455 0.6242
M 4.0699 1.1646 0.5917 0.4083 1.8814 0.6345
T10 O 1.3416 5.8264 5.6180 44.8413 9.1621 16.6656
N 0.5326 4.6143 2.8479 50.9191 5.3271 10.3754
M 0.3492 2.7033 1.9654 36.5806 7.6850 14.0528

Fig.16

Time efficiency contrast figures"

1 杨建伟, 李沛遥 . 基于分数阶矩的仿射不变特征提取[J]. 自动化学报, 2015, 41(12):2147⁃2154.
Yang Jian-wei , Li Pei-yao . Affine invariant feature extraction based on fractional order moment[J]. Acta Electronica Sinica, 2015, 41(12):2147-2154
2 缪君, 储珺, 张桂梅 . 一种仿射不变的直线描述子与直线匹配[J]. 电子学报, 2015, 43(12):2505⁃2512.
Miao Jun , Chu Jun , Zhang Gui⁃mei . An affine invariant line descriptor and line matching[J]. Chinese Journal of Electronics, 2015, 43(12): 2505⁃2512.
3 Heikkilä J . Multi⁃scale autoconvolution for affine invariant pattern recognition[C]∥The 16th International Conference on Pattern Recognition, Quebec City, Canada, 2002:10119.
4 Rahtu E , Salo M , Heikkila J . Affine invariant pattern recognition using multiscale autoconvolution[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2005, 27(6):908⁃918.
5 Kannala J , Rahtu E , Heikkila J . Affine registration with multi⁃scale autoconvolution[C]∥IEEE International Conference on Image Processing, Genova, Italy, 2005:1064⁃7.
6 Rahtu E , Salo M , Heikkilä J . Multiscale autoconvolution histograms for affine invariant pattern recognition[C]∥British Machine Vision Conference, Edinburgh, Uk, 2006:1059⁃1068.
7 Rahtu E , Salo M , Heikkil J , et al . Generalized affine moment invariants for object recognition[C]∥International Conference on Pattern Recognition, Hong Kong, 2006:634⁃637.
8 Hu Ming Kuei . Visual pattern recognition by moment invariants[J]. IRE Transactions on Information Theory, 1962, 8(2):179⁃187.
9 翟凤文, Muhammad Asim Azim, 王阳萍, 等 . Zernike矩快速算法的修正[J]. 吉林大学学报:工学版, 2014, 44(6):1860⁃1866.
Zhai Feng⁃wen , Muhammad Asim Azim, Wang Yang⁃ping , et al . Amendment of Zernike moment fast computation[J]. Journal of Jilin University(Engineering and Technology Edition), 44(6): 1860⁃1866.
10 李晖晖, 滑立, 杨宁, 等 . 基于MSA特征和模拟退火优化的遥感图像多目标关联算法[J]. 吉林大学学报:工学版, 2015, 45(4):1353⁃1359.
Li Hui⁃hui , Hua Li , Yang Ning , et al . Multi⁃target association algorithm for remote sensing images based on MSA features and simulated annealing optimization[J]. Journal of Jilin University(Engineering and Technology Edition), 2015, 45(4):1353⁃1359.
11 吴喆, 曾接贤, 高琪琪 . 显著图和多特征结合的遥感图像飞机目标识别[J]. 中国图象图形学报, 2017, 22(4):532⁃541.
Wu Zhe ， Zeng Jie⁃xian ， Gao Qi⁃qi . Aircraft target recognition in remote sensing images based on saliency images and multi⁃feature combination[J]. Journal of Image and Graphics, 2017, 22(4):532⁃541.
12 张洁玉, 陈强, 白小晶, 等 . 仿射不变的多尺度自卷积熵提取方法[J]. 计算机辅助设计与图形学学报, 2009, 21(9):1328⁃1332.
Zhang Jie⁃yu , Chen Qiang , Bai Xiao⁃jing ,et al . Affine invariant feature extraction based on multi⁃scale auto⁃convolution entropy[J]. Journal of Computer⁃Aided Design & Computer Graphics, 21(9):1328⁃1332.
13 Rahtu E , Salo M , Heikkilä J . A new efficient method for producing global affine invariants[J]. LNCS, 2005, 3617:407⁃414.
14 贾建华, 焦李成, 黄文涛 . 一种基于质心不变特性的仿射不变纹理特征提取算法[J]. 电子学报, 2008, 36(10):1910⁃1915.
Jia Jian⁃hua , Jiao Li⁃cheng , Huang Wen⁃tao . Affine invariant texture feature extraction based on invariant centroid[J]. Acta Electronica Sinica, 2008, 36(10):1910⁃1915.
15 Petrou M , Kadyrov A . Features invariant to affine distortions from the trace transform[C]∥Proceeding of the IEEE Conference on Image Processing, Thessaloniki, Greece, 2001: 852⁃855.
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