模糊支持向量机及其在场景图像处理中的应用研究
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摘要
对于工作在复杂环境中的机器人,有效的场景图像处理能够使机器人获得良好的场景感知和理解能力,是其实现自主导航并自主探索环境的前提条件。由于场景图像的随机性、多样性、复杂性等特点,同时机器人可得到的视觉先验知识不稳定,且对复杂环境中多类物体的识别技术仍不成熟,因此如何设计有效的场景图像处理方法成为机器视觉领域具有挑战性的研究热点。
支持向量机算法建立在严格的理论基础之上,在解决小样本、非线性和高维模式识别问题中表现出了许多特有的优势。近年来,支持向量机已被成功地应用于系统辨识、机器人控制、遥感图像处理、经济管理等研究领域。然而,现有的支持向量机应用研究忽视了一个重要问题,那就是如何将专家逻辑经验与支持向量机方法有效结合起来。在实际的工程应用中,我们通常对某些待分析的数据信号拥有一定的先验知识,对待处理的信息有着部分定性经验判断。因此,将专家经验知识与支持向量机方法有机结合起来,研究合适的模糊支持向量机模型,是支持向量机算法面向工程技术领域的一个亟待解决的重要问题。
本文结合场景图像处理应用,研究了模糊支持向量机算法的一些相关问题。论文主要的工作及研究成果如下：
1)提出了三论域的模糊支持向量回归模型,提高了传统SVR算法处理带不确定信息问题的能力。模型采用全新的第三论域用于引入有用的先验知识,将传统核函数和模糊隶属度函数融合到三论域模糊核中,利用三论域的模糊核函数实现对输入、输出和不确定信息的统一分析。三论域模糊目标函数的定义及其优化过程则为三论域模糊支持向量回归模型提供了完善的理论支持。实验结果表明：三论域模糊支持向量回归模型在处理场景图像去噪问题上具有较好的应用效果。
2)研究了样本分布的不确定性对最小二乘支持向量回归算法的影响,同时考虑样本的局部相似性,提出了基于样本分布密度加权的模糊密度权最小二乘支持向量回归(Fuzzy Density Weighted Least Squares Support Vector Regression, FDW-LSSVR)场景图像去噪算法。该算法基于模糊逻辑系统对样本的模糊密度权进行了有效设计。根据输入样本、输出样本的分布密度模糊矩阵及模糊规则,对模糊密度权进行模糊推理,以得到给定样本的模糊密度权。实验结果表明：FDW-LSSVR场景图像去噪算法在客观评价和实时性方面都有较好的表现。
3)在核函数中采用Type-2模糊集的设计准则,提出了系统化的区间Type-2模糊核支持向量机(Interval Type-2Fuzzy Kernel based Support Vector Machine, IT2FK-SVM)场景图像分类算法。首先,在场景图像中提取灰度特征图、边缘特征图和方向特征图,构建场景图像的分类特征向量。然后,基于概率模糊核的主成分分析方法对第一阶段所建立的较高维分类特征向量进行有效降维。最后通过区间Type-2模糊核的设计,提高模糊核的SVM分类算法的鲁棒性。实验结果表明：IT2FK-SVM场景图像分类算法在样本受噪声污染、不同视觉角度、不同光照条件等不确定条件下,可达到较高的分类精度。
4)针对多源特征输入分类问题,提出了分组特征嵌套核支持向量机(Grouping Feature and Nesting Kernel based Support Vector Machine, GFNK-SVM)场景图像分割算法。该算法首先提取场景图像像素点的灰度特征,梯度特征和C1标准模型特征(Standard Model Feature, SMF),构成像素样本集。再基于聚类有效性的区间Type-2模糊C-means (Cluster Validity based Interval Type-2Fuzzy C-Means, CV-IT2FCM)聚类算法实现样本选择,利用Type-2模糊度量准则得到模糊的聚类边界,提高了聚类结果的鲁棒性和可靠性。最后基于嵌套核的SVM分类算法对像素点进行有效分类,实现对场景图像的分割。BSDS数据库和机器人场景图像数据库的实验结果表明：GFNK-SVM场景图像分割算法能够获得较好的分割结果。
Scene perception and understanding are indispensable abilities for mobile robot to navigate and explore complex environment automatically, which is dependent on the results of scene images processing algorithms. However, the nature of scene images is always random, diversified and complex. Furthermore, the prior vision information of scene images is quite often poor and the technology of object recognition in scene images is still immature. It is interesting to study how we can design efficient processing algorithms of scene images for humanoid robot.
Support Vector Machine (SVM) build on the strict theoretical basis, which has been proved to prossess the remarkable characteristics of good generalization performance, the small sample, nonlinear, and high dimension. Recently, it has been successfully used to the system recognition, robot control, remote sensing image processing and economy management fields, etc. However, the problem of integrating SVM and experiential knowledge from experts is unfortunately ignored in the previous works. In many engineering applications, it is possible to obtain prior information and qualitative analysis from unseen data. Thus, it is interesting to study fuzzy theory with SVM methods, which combine the experiential logic of experts into SVM model.
Some problems, which related to support vector machine, have been studied in the paper, and the proposed algorithms have been applied to process scene image. The main contributions of this paper are as follows:
1) A novel Three-Domain Fuzzy Support Vector Regression (3DFSVR) is proposed, which will enhance the potentials of Two-Domain Support Vector Regression (2DSVR) to handle uncertainties. When compared with traditional two-domain SVR, the major advantage of3DFSVR is able to use the prior knowledge via the novel fuzzy domain to analyze uncertain data and signals. The Three-Domain Fuzzy Kernel Function (3DFKF) provides a solution to process uncertainties and input-output data information simultaneously, which also integrate the kernel and fuzzy membership function into a three-domain function. Definition and solution of Fuzzy Convex Optimization problem (FCOP) are presented to construct the whole theoretical framework. Experiments and simulation results show the effectiveness of3DFSVR for the uncertain image denoising.
2) It is interesting to study how we can design denoising algorithm which not only can deal with the uncertainty of sample density but also take account of local similarity in images. A new Fuzzy Density Weight based Least Squares Support Vector Regression (FDW-LSSVR) denoising algorithm for humanoid robot which assigns fuzzy membership values for feature vector in order to reduce the effect of uncertainty of sample density on LSSVR is proposed. It also present a new method for the design of fuzzy membership, which is designed via fuzzy theory based on the sample density. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques.
3) By integrating the kernel design with Type-2fuzzy sets, a systematic design methodology of Interval Type-2Fuzzy Kernel based Support Vector Machine (IT2FK-SVM) classification for scene images is presented to improve robustness and selectivity in the humanoid robot vision. Firstly, scene images are represented as high dimensional vector extracted from intensity, edge and orientation feature maps by biological-vision feature extraction method. Furthermore, a novel Probabistic Fuzzy Kernel based Principal Component Analysis (PFK-PCA) method is proposed to select the prominent variables from the high-dimensional scene image representation. Finally, an IT2FK-SVM classifier is developed for the comprehensive learning of scene images in complex environment. IT2FK-SVM is able to deal with uncertainties when scene images are corrupted by various noises and captured by different view angles. The proposed IT2FK-SVM method yields over92%classification rates for all cases. Moreover, it even achieves98%classification rate on the newly built dataset with common light case.
4) A framework of Grouping-Feature and Nesting-Kernel Support Vector Machine (GFNK-SVM) methodology is presented to achieve a more reliable and robust segmentation performance for humanoid robot. Firstly, the pixel wise intensity, gradient and C1SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of GFNK-SVM model. Then, a new clustering method, which we called as Cluster Validity-Interval Type-2Fuzzy C-Means (CV-IT2FCM) clustering algorithm, is proposed to achieve sample selection by integrating a Type-2fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Finally, by integrating SVM with a novel nesting-kernel, a systematic GFNK-SVM framework is presented and its model is trained as classifier for scene images segmentation. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.
支持向量机算法建立在严格的理论基础之上,在解决小样本、非线性和高维模式识别问题中表现出了许多特有的优势。近年来,支持向量机已被成功地应用于系统辨识、机器人控制、遥感图像处理、经济管理等研究领域。然而,现有的支持向量机应用研究忽视了一个重要问题,那就是如何将专家逻辑经验与支持向量机方法有效结合起来。在实际的工程应用中,我们通常对某些待分析的数据信号拥有一定的先验知识,对待处理的信息有着部分定性经验判断。因此,将专家经验知识与支持向量机方法有机结合起来,研究合适的模糊支持向量机模型,是支持向量机算法面向工程技术领域的一个亟待解决的重要问题。
本文结合场景图像处理应用,研究了模糊支持向量机算法的一些相关问题。论文主要的工作及研究成果如下：
1)提出了三论域的模糊支持向量回归模型,提高了传统SVR算法处理带不确定信息问题的能力。模型采用全新的第三论域用于引入有用的先验知识,将传统核函数和模糊隶属度函数融合到三论域模糊核中,利用三论域的模糊核函数实现对输入、输出和不确定信息的统一分析。三论域模糊目标函数的定义及其优化过程则为三论域模糊支持向量回归模型提供了完善的理论支持。实验结果表明：三论域模糊支持向量回归模型在处理场景图像去噪问题上具有较好的应用效果。
2)研究了样本分布的不确定性对最小二乘支持向量回归算法的影响,同时考虑样本的局部相似性,提出了基于样本分布密度加权的模糊密度权最小二乘支持向量回归(Fuzzy Density Weighted Least Squares Support Vector Regression, FDW-LSSVR)场景图像去噪算法。该算法基于模糊逻辑系统对样本的模糊密度权进行了有效设计。根据输入样本、输出样本的分布密度模糊矩阵及模糊规则,对模糊密度权进行模糊推理,以得到给定样本的模糊密度权。实验结果表明：FDW-LSSVR场景图像去噪算法在客观评价和实时性方面都有较好的表现。
3)在核函数中采用Type-2模糊集的设计准则,提出了系统化的区间Type-2模糊核支持向量机(Interval Type-2Fuzzy Kernel based Support Vector Machine, IT2FK-SVM)场景图像分类算法。首先,在场景图像中提取灰度特征图、边缘特征图和方向特征图,构建场景图像的分类特征向量。然后,基于概率模糊核的主成分分析方法对第一阶段所建立的较高维分类特征向量进行有效降维。最后通过区间Type-2模糊核的设计,提高模糊核的SVM分类算法的鲁棒性。实验结果表明：IT2FK-SVM场景图像分类算法在样本受噪声污染、不同视觉角度、不同光照条件等不确定条件下,可达到较高的分类精度。
4)针对多源特征输入分类问题,提出了分组特征嵌套核支持向量机(Grouping Feature and Nesting Kernel based Support Vector Machine, GFNK-SVM)场景图像分割算法。该算法首先提取场景图像像素点的灰度特征,梯度特征和C1标准模型特征(Standard Model Feature, SMF),构成像素样本集。再基于聚类有效性的区间Type-2模糊C-means (Cluster Validity based Interval Type-2Fuzzy C-Means, CV-IT2FCM)聚类算法实现样本选择,利用Type-2模糊度量准则得到模糊的聚类边界,提高了聚类结果的鲁棒性和可靠性。最后基于嵌套核的SVM分类算法对像素点进行有效分类,实现对场景图像的分割。BSDS数据库和机器人场景图像数据库的实验结果表明：GFNK-SVM场景图像分割算法能够获得较好的分割结果。
Scene perception and understanding are indispensable abilities for mobile robot to navigate and explore complex environment automatically, which is dependent on the results of scene images processing algorithms. However, the nature of scene images is always random, diversified and complex. Furthermore, the prior vision information of scene images is quite often poor and the technology of object recognition in scene images is still immature. It is interesting to study how we can design efficient processing algorithms of scene images for humanoid robot.
Support Vector Machine (SVM) build on the strict theoretical basis, which has been proved to prossess the remarkable characteristics of good generalization performance, the small sample, nonlinear, and high dimension. Recently, it has been successfully used to the system recognition, robot control, remote sensing image processing and economy management fields, etc. However, the problem of integrating SVM and experiential knowledge from experts is unfortunately ignored in the previous works. In many engineering applications, it is possible to obtain prior information and qualitative analysis from unseen data. Thus, it is interesting to study fuzzy theory with SVM methods, which combine the experiential logic of experts into SVM model.
Some problems, which related to support vector machine, have been studied in the paper, and the proposed algorithms have been applied to process scene image. The main contributions of this paper are as follows:
1) A novel Three-Domain Fuzzy Support Vector Regression (3DFSVR) is proposed, which will enhance the potentials of Two-Domain Support Vector Regression (2DSVR) to handle uncertainties. When compared with traditional two-domain SVR, the major advantage of3DFSVR is able to use the prior knowledge via the novel fuzzy domain to analyze uncertain data and signals. The Three-Domain Fuzzy Kernel Function (3DFKF) provides a solution to process uncertainties and input-output data information simultaneously, which also integrate the kernel and fuzzy membership function into a three-domain function. Definition and solution of Fuzzy Convex Optimization problem (FCOP) are presented to construct the whole theoretical framework. Experiments and simulation results show the effectiveness of3DFSVR for the uncertain image denoising.
2) It is interesting to study how we can design denoising algorithm which not only can deal with the uncertainty of sample density but also take account of local similarity in images. A new Fuzzy Density Weight based Least Squares Support Vector Regression (FDW-LSSVR) denoising algorithm for humanoid robot which assigns fuzzy membership values for feature vector in order to reduce the effect of uncertainty of sample density on LSSVR is proposed. It also present a new method for the design of fuzzy membership, which is designed via fuzzy theory based on the sample density. Extensive experimental results demonstrate that our method can obtain better performances in terms of both subjective and objective evaluations than those state-of-the-art denoising techniques.
3) By integrating the kernel design with Type-2fuzzy sets, a systematic design methodology of Interval Type-2Fuzzy Kernel based Support Vector Machine (IT2FK-SVM) classification for scene images is presented to improve robustness and selectivity in the humanoid robot vision. Firstly, scene images are represented as high dimensional vector extracted from intensity, edge and orientation feature maps by biological-vision feature extraction method. Furthermore, a novel Probabistic Fuzzy Kernel based Principal Component Analysis (PFK-PCA) method is proposed to select the prominent variables from the high-dimensional scene image representation. Finally, an IT2FK-SVM classifier is developed for the comprehensive learning of scene images in complex environment. IT2FK-SVM is able to deal with uncertainties when scene images are corrupted by various noises and captured by different view angles. The proposed IT2FK-SVM method yields over92%classification rates for all cases. Moreover, it even achieves98%classification rate on the newly built dataset with common light case.
4) A framework of Grouping-Feature and Nesting-Kernel Support Vector Machine (GFNK-SVM) methodology is presented to achieve a more reliable and robust segmentation performance for humanoid robot. Firstly, the pixel wise intensity, gradient and C1SMF features are extracted via the local homogeneity model and Gabor filter, which would be used as inputs of GFNK-SVM model. Then, a new clustering method, which we called as Cluster Validity-Interval Type-2Fuzzy C-Means (CV-IT2FCM) clustering algorithm, is proposed to achieve sample selection by integrating a Type-2fuzzy criterion in the clustering optimization process to improve the robustness and reliability of clustering results by the iterative optimization. Finally, by integrating SVM with a novel nesting-kernel, a systematic GFNK-SVM framework is presented and its model is trained as classifier for scene images segmentation. Experiments on the BSDS dataset and real natural scene images demonstrate the superior performance of our proposed method.
引文
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