一种基于T2FPSO的type-2模糊支持向量机场景分类方法

doi:10.16182/j.issn1004731x.joss.201612007

系统仿真学报 ›› 2016, Vol. 28 ›› Issue (12): 2925-2933.doi: 10.16182/j.issn1004731x.joss.201612007

一种基于T2FPSO的type-2模糊支持向量机场景分类方法

徐淑琼, 袁从贵

东莞职业技术学院,广东东莞 523808

收稿日期:2016-02-26 修回日期:2016-05-24 出版日期:2016-12-08 发布日期:2020-08-13
第一作者简介:Xu Shuqiong(1981-), Female, Guangdong, China, Doctor, Lecturer, Research interests include intelligent control systems and signal processing.
基金资助:
The projects of Guangdong Natural Science Foundation (2014A030310380, 2015A030310257) , The project of Dongguan Polytechnic Foundation (2015a05)

Effective T2FPSO-Based T2FSVM Scene Classification Algorithm

Xu Shuqiong, Yuan Conggui

Dongguan Polytechnic, Dongguan 523808, China

Received:2016-02-26 Revised:2016-05-24 Online:2016-12-08 Published:2020-08-13
About author:Xu Shuqiong(1981-), Female, Guangdong, China, Doctor, Lecturer, Research interests include intelligent control systems and signal processing.
Supported by:
The projects of Guangdong Natural Science Foundation (2014A030310380, 2015A030310257) , The project of Dongguan Polytechnic Foundation (2015a05)

摘要/Abstract

摘要： 为提高机器视觉识别的选择性和鲁棒性,给出了基于T2FPSO优化的T2FSVM场景分类方法。算法中,设计了type-2模糊支持向量机模型以提高其泛化能力并得到正确的场景分类信息;为提高PSO在不确定环境中的优化能力,构建了融合type-2模糊集概念的T2FPSO优化算法,并采用区间type-2模糊逻辑系统推理得到其惯性权值。实验结果表明所提出的场景分类方法可对不确定信息进行有效处理。

关键词: 场景分类, type-2模糊粒子群优化, type-2模糊支持向量机, type-2模糊逻辑系统

Abstract: A systematic design methodology of Type-2 Fuzzy Particle Swarm Optimization (T2FPSO) based Type-2 Fuzzy Support Vector Machine (T2FSVM) classification system was proposed for scene image to improve selectivity and robustness in the machine vision. In the novel classification system, the T2FSVM model was presented to realize a comprehensive learning of the correct class and show the superiority of the generalization capability for classification problem. Furthermore, in order to improve the performance of PSO on complex uncertain environments, the type-2 fuzzy concept was incorporated to PSO to construct T2FPSO searching algorithm, in which the interval type-2 fuzzy inertia weight was designed using an Interval Type-2 Fuzzy Logic System (IT2FLS). Experimental studies indicate that the T2FPSO-T2FSVM approach is effective to deal with uncertainties for scene classification, when scene images are corrupted by the hybrid noises or captured by different view angels and light conditions.

Key words: scene classification, type-2 fuzzy particle swarm optimization (T2FPSO), type-2 fuzzy support vector machine (T2FSVM), type-2 fuzzy logic system (T2FLS)

中图分类号:

TP391.9

徐淑琼,袁从贵 . 一种基于T2FPSO的type-2模糊支持向量机场景分类方法[J]. 系统仿真学报, 2016, 28(12): 2925-2933.

Xu Shuqiong,Yuan Conggui . Effective T2FPSO-Based T2FSVM Scene Classification Algorithm[J]. Journal of System Simulation, 2016, 28(12): 2925-2933.

参考文献

[1] Elbanhawi M, Simic M.Sampling-Based Robot Motion Planning: A Review[J]. IEEE Access (S2169-3536), 2014, 2(1): 56-77.
[2] Yanik P M, Manganelli J, Merino J, et al.A Gesture Learning Interface for Simulated Robot Path Shaping With a Human Teacher[J]. IEEE Trans. on Human- Machine Systems (S1094-6977), 2014, 44(1): 41-54.
[3] Soohwan K, Jonghyuk K.Occupancy Mapping and Surface Reconstruction Using Local Gaussian Processes with Kinect Sensors[J]. IEEE Trans. on Cybernetics (S1083-4419), 2013, 43(5): 1335-1346.
[4] Theriault C, Thome N, Cord M, et al.Perceptual Principles for Video Classification With Slow Feature Analysis[J]. IEEE Journal of Selected Topics in Signal Processing (S1932-4553), 2014, 8(3): 428-437.
[5] Yang X, Zhang T, Xu C, et al.Automatic Visual Concept Learning for Social Event Understanding[J]. IEEE Trans. on Multimedia (S1520-9210), 2015, 17(3): 346-358.
[6] Lin L, Zhang R, Duan X.Adaptive Scene Category Discovery with Generative Learning and Compositional Sampling[J]. IEEE Trans. on Circuits and Systems for Video Technology (S1051-8215), 2015, 25(2): 251-260.
[7] Chen S, Tian Y.Pyramid of Spatial Relations for Scene-Level Land Use Classification[J]. IEEE Trans. on Geoscience and Remote Sensing (S0196-2892), 2015, 53(4): 1947-1957.
[8] Wang M, Song T.Remote Sensing Image Retrieval by Scene Semantic Matching[J]. IEEE Trans. on Geoscience and Remote Sensing (S0196-2892), 2013, 51(5): 2874-2886.
[9] Qi X, Xiao R, Li C, et al.Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence (S0162-8828), 2014, 36(11): 2199-2213.
[10] Chern Hong L, Risnumawan A, Chee Seng C, et al.A Scene Image is Nonmutually Exclusive—A Fuzzy Qualitative Scene Understanding[J]. IEEE Trans. on Fuzzy Systems (S1063-6706), 2014, 22(6): 1541-1556.
[11] Liu Z, Xu S, Zhang Y, et al.Interval type-2 Fuzzy Kernel based Support Vector Machine Algorithm for Scene Classification of Humanoid Robot[J]. Soft Computing (S1432-7643), 2014, 18: 589-606.
[12] Yu J, Zhou H, Gao X.Machine Learning and Signal Processing for Human Pose Recovery and Behavior Analysis[J]. Signal Processing (S0165-1684), 2015, 110: 1-4.
[13] Torheim T, Malinen E, Kvaal K, et al.Classification of Dynamic Contrast Enhanced MR Images of Cervical Cancers Using Texture Analysis and Support Vector Machines[J]. IEEE Trans. on Medical Imaging (S0278-0062), 2014, 33(8): 1648-1656.
[14] Patra S, Bruzzone L.A Novel SOM-SVM-Based Active Learning Technique for Remote Sensing Image Classification[J]. IEEE Trans. on Geoscience and Remote Sensing (S0196-2892), 2014, 52(11): 6899-6920.
[15] Ruiz P, Mateos J, Camps-Valls G, et al.Bayesian Active Remote Sensing Image Classification[J]. IEEE Trans. on Geoscience and Remote Sensing (S0196-2892), 2014, 52(4): 2186-2196.
[16] Liu Z, Xu S, Zhang Y, et al.A Multiple-Feature and Multiple-Kernel Scene Segmentation Algorithm for Humanoid Robot[J]. IEEE Trans. on Cybernetics (S1083-4419), 2014, 44(11): 2232-2240.
[17] Zhang S, He F, Zhang Y, et al.Geometric Active Contour based Approach for Segmentation of High-resolution Spaceborne SAR Images[J]. Journal of Systems Engineering and Electronics (S1004-4132), 2015, 26(1): 69-76.
[18] Wang L, Liu Z, Chen C L P, et al. Energy-Efficient SVM Learning Control System for Biped Walking Robots[J]. IEEE Trans. on Neural Networks and Learning Systems (S1045-9227), 2013, 24(5): 831-837.
[19] Wang L, Liu Z, Chen C L P, et al. Support Vector Machine based Optimal Control for Minimizing Energy Consumption of Biped Walking Motions[J]. International Journal of Precision and Engineering (S2234-7593), 2012, 13(11): 1975-1981.
[20] Juang C F, Huang R B, Cheng W.An Interval Type-2 Fuzzy-Neural Network with Support-Vector Regression for Noisy Regression Problems[J]. IEEE Trans. on Fuzzy Systems (S1063-6706), 2010, 18(4): 686-699.
[21] Maulik U, Bandyopadhyay S, Saha I.Integrating Clustering and Supervised Learning for Categorical Data Analysis[J]. IEEE Trans. on Systems, Man, and Cybernetics (S1083-4427), 2010, 40(4): 664-675.
[22] Melgani F, Bazi Y.Classification of Electrocardiogram Signals with Support Vector Machines and Particle Swarm Optimization[J]. IEEE Trans. on Information Technology in Biomedicine (S1089-7771), 2008, 12(5): 667-677.

一种基于T2FPSO的type-2模糊支持向量机场景分类方法

Effective T2FPSO-Based T2FSVM Scene Classification Algorithm

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价

[1]	黄涛, 张智, 丁玉杰, 陈艳波, 王晶, 张文倩. 考虑动态频率安全与N-k故障的鲁棒应急调度方法[J]. 系统仿真学报, 2025, 37(12): 2981-2993.
[2]	张润昭, 陈艳波, 黄涛, 田昊欣, 强涂奔, 张智. 基于异构负荷特征解析预测的虚拟电厂调度方法[J]. 系统仿真学报, 2025, 37(12): 2994-3006.
[3]	于祥星, 赵艳东, 张宝琳. 基于电涡流NES的海上风机塔架振动控制[J]. 系统仿真学报, 2025, 37(12): 3007-3017.
[4]	李斌, 王于绰. 基于多策略融合的光伏系统故障诊断方法[J]. 系统仿真学报, 2025, 37(12): 3018-3032.
[5]	李孝斌, 胡冰, 尹超, 李波, 马军. 基于时空图卷积的汽车配件供应链需求预测与仿真分析[J]. 系统仿真学报, 2025, 37(12): 3060-3074.
[6]	彭艺, 雷云揆, 杨青青, 李辉, 王健明. 改进PID搜索算法的山地环境无人机路径规划[J]. 系统仿真学报, 2025, 37(12): 3075-3086.
[7]	陈逸, 邱思航, 朱正秋, 季雅泰, 赵勇, 鞠儒生. 基于启发式的人-大模型协作寻源方法[J]. 系统仿真学报, 2025, 37(12): 3112-3127.
[8]	索婧怡, 卢柏宏, 屈澈. 影视LED光源光强分布测定及其在游戏引擎中的仿真研究[J]. 系统仿真学报, 2025, 37(12): 3140-3151.
[9]	龚建兴, 胡海, 任海慧, 吴瑞祥. 面向虚实结合的军事训练系统互操作模型与运用[J]. 系统仿真学报, 2025, 37(12): 3161-3175.
[10]	徐智霞, 王蕊, 孙楠, 何兵, 沈晓卫, 朱晓菲. 基于改进遗传算法的协同干扰资源分配问题研究[J]. 系统仿真学报, 2025, 37(12): 3176-3189.
[11]	刘翔, 金乾坤. 基于PAC-Bayes的多目标强化学习A2C算法研究[J]. 系统仿真学报, 2025, 37(12): 3212-3223.
[12]	杨兰英, 李超, 邹海锋, 万江涛, 张仁强, 刘惠, 卢宏. 基于改进蚁群算法与A*算法相融合的机器人路径规划优化[J]. 系统仿真学报, 2025, 37(11): 2956-2965.
[13]	苏筱婷, 张小威, 田义, 李奇, 王帅豪. 星光导航动态仿真场景时序设计方法研究[J]. 系统仿真学报, 2025, 37(11): 2946-2955.
[14]	张志利, 刘瑾, 周召发, 梁哲, 张云昊. 基于ISCSO-BP神经网络模型的光纤陀螺温度补偿技术研究[J]. 系统仿真学报, 2025, 37(11): 2904-2917.
[15]	陈际同, 周佳加, 吴迪, 江海龙. 基于TD3-RRT的特殊环境下USV路径规划算法研究[J]. 系统仿真学报, 2025, 37(11): 2888-2903.