基于复杂网络的图像目标识别方法研究
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摘要
图像目标识别技术是人工智能研究的重点领域之一。在视频监控、人机交互、交通监控、行为识别、自动导航等方面都有大量成功应用的例子。目前已经形成了许多有价值的目标识别方法。根据识别过程用到的目标对象特征,可以将目标识别方法大致划分为基于区域的算法、基于轮廓的算法、基于模型的算法以及基于特征的算法四类。根据所采用算法的不同,又可以将目标识别方法大致划分为基于滤波理论的目标识别方法、基于Mean Shift的目标识别方法、基于偏微分方程的目标识别方法等三类。值得注意的是,由于这些识别方法多与图像中点的位置和顺序紧密相关,因此在面对图像平面旋转不变性、平移不变性、缩放不变性等特性时,其识别效果都有不同程度的减弱,在实际应用中,图像轮廓轻微变化、光照强度小幅度变化以及局部遮挡等因素都会对识别效果产生不良影响。
复杂网络是复杂系统理论中的重要研究对象,复杂网络理论研究受到越来越多研究者的重视,相关的概念和方法都是当前的研究热点。复杂网络利用数学图论建立模型,仅考虑节点间的相对位置等拓扑关系,对网络节点间的顺序关系、节点所处位置等关注较少,网络图的整体旋转、平移等对复杂网络拓扑特性没有影响。因此,如果能够利用复杂网络作为平面图像边界形状的描述模型,并在此基础上建立形状识别算法,那么这种算法将能够有效地适应图像边界形状的改变。
本文在现有相关研究基础上,将复杂网络方法应用于图像目标识别领域,针对形状轮廓识别和灰度图像识别等应用环境,提出一类基于复杂网络的图像目标识别方法。通过将基于轮廓的图像识别方法与复杂网络方法优点相融合,本文方法既保留基于轮廓的识别方法所具有的过程简单、识别效率高等特点,又充分发挥复杂网络方法仅考虑网络拓扑结构,与节点位置、顺序无关等特点,克服了图像轮廓轻微变化、光照强度小幅度变化以及局部遮挡等因素对识别方法的影响,使得识别方法具有平面旋转不变性、平移不变性、缩放不变性以及一定程度的容噪性。
本文研究内容属于二维序列图像中的目标识别。通过获取目标的轮廓,对运动目标进行识别,目标行为的跟踪、分析、描述和理解提供可靠的数据支持。本文方法的主要技术路线是首先对待识别的图像提取形状轮廓和灰度轮廓组,分别保留图像的形状特征和颜色特征。然后将上述轮廓以图的形式表示,利用复杂网络理论建立相应的网络模型,并计算与复杂网络相关的参数。最后通过对所有网络模型提取特征参数,汇集形成识别参数,产生图像目标识别算法用于对象目标的识别和分类。
本文研究内容的创新性主要体现在以下几个方面：
(1)融合复杂网络与轮廓识别方法。利用复杂网络方法抽取目标的轮廓拓扑信息,形成识别参数,将复杂网络方法的优点融入到基于轮廓的目标识别方法中,简化目标网络模型的复杂程度,增强识别方法的容噪性,形成一种有效的目标识别方法。
(2)控制复杂网络规模。本文通过利用图像轮廓、使用简单网络参数、改进建模步骤等方式,从多个方面控制复杂网络模型的规模,减少识别方法占用的存储空间,缩短计算时间。在更简单的网络模型基础上,获取尽可能多的拓扑信息用于目标识别。
(3)改进图像轮廓提取方法。本文提出针对灰度图像分别提取形状轮廓、灰度轮廓用于复杂网络目标识别的新思路,综合利用灰度图像中目标对象的形状特征、色彩特征,提升目标间的区别度,提升识别效率。其中,在灰度轮廓提取方面,本文提出一种使用简单的二值化图像去除邻接点的轮廓提取方法,既克服了二值化图像像素点多的缺点,又在一定程度上保留了图像的色彩信息。
(4)调整阈值参数和识别参数。本文在阈值参数和识别参数选取方面做了改进。提出一种距离阈值判定(DTD)方法,利用样本组的轮廓计算判定参数,用于协助确定距离阈值的取值范围,减少主观判断造成的识别效果不确定性。选择识别参数时,使用了基于节点度的一系列简单的网络参数,通过一次计算得到一组参数,既反映了更丰富的网络模型的拓扑特征,也减少了计算量。
仿真实验数据证明本文方法具有对轮廓图精确度依赖性较低、复杂网络规模小、阈值参数少、能有效适应边界形状改变等优点,在给定的形状轮廓识别、灰度图像识别应用方面具有较高的准确率。本文提出的DTD方法在协助确定距离阈值方面具有有效性。
本文受国家基金项目(61273219)、广东省自然科学基金项目(8151009001000061)、广东省自然科学基金团队项目(8351009001000002)等资助。
Image Object Recognition technology is one of the most important areas of Artificial Intelligence research. It had lots of successful applications such as Video Monitoring, Human-Computer Interaction, Traffic Monitoring, Behavior Recognition, Automatic Navigation, etc. There are several valuable methods for object recognition. According to the features of target object which are used in the recognition process, the object recognition methods can be roughly divided into four categories, such as the Region-Based algorithm, the Contour-Based algorithm, the Model-Based algorithm and the Feature-Based algorithm. The algorithms which are mainly used in object recognition methods can be divided into the Filtering theory, the Mean Shift method, the Partial Differential Equations method, etc. It should be noted that these recognition methods are closely related to the location and sequence of the pixels in the image. So the recognition rate will be reduced by rotation, translational and scaling. In the practical application, the slight changes of the image contour, small variations of the light intensity as well as partial occlusion and other factors will produce adverse effects on the recognition results.
Getting more and more attention by the researchers, Complex Network theory becomes important in the research of the Complex System. The concepts and methods of it have become research focus recently. Modeling by the mathematical Graph theory,Complex Network theory considers only the topological relationships, such as the relative position between nodes. It pays less attention on the location and sequence of the nodes. The overall rotation and translation will not affect the topology of the network diagram. So, a shape recognition algorithm, which uses the Complex Network theory as the description model to the shape of image boundary, may be able to effectively adapt to the changes in the image boundary.
In this work, the Complex Network theory was used in the Image Object Recognition. Based on the existing researches, an image object recognition method based on Complex Network was applied to the shape contour recognition and gray image recognition applications. The proposed method could have the advantages of both Contour-Based recognition method and Complex Network theory, including simple process, high identify efficiency and regardless of the position or order of the nodes in the image. The proposed method can overcome the slight changes of the image contours and the light intensity as well as partial occlusion. In this way, the recognition method is rotation invariance, translation invariance,scale invariance and noise tolerance.
This work takes the Image Object Recognition method in the two-dimensional image sequences into account. By extracting contours from the object, the proposed method recognizes the object from the image sequences. This work will provide reliable data for target tracking, behavior analysis, description and understanding. The main technical route of the proposed method could be described as follow. Firstly, the shape contour and color contours are extracted separately from the image object. Then, the contours are described into graphs and modeled by the Complex Network methodology. Finally, the characteristic parameters are calculated for each network model. And a feature vector for object recognition is extracted for image object recognition and classification.
This work mainly includes the following aspects:
(1) Combining the Complex Network theory and Contour Recognition method together. This work extracts the target identification parameters by the topology information of the contour with Complex Network theory. In this way, the method has the advantages of the Complex Network theory and the Contour Recognition method. With a simpler network model, the proposed method becomes noise tolerance and reaches effective recognition result.
(2) Controlling the complexity of the network. In this work, several methods are used to control the size of the complex network model. Firstly, the proposed method reduces the number of nodes in the network model by extracting the contours from the image. Secondly, the proposed method uses the simpler network parameters in order to reducing the complexity of calculating. Thirdly, the process of modeling is improved. By these changes, the storage space occupied by the recognition method is reduced and the calculation time is shortened. Topology information, as much as possible, is extracted from these simpler network models for object recognition.
(3) Improving the image contour extraction method. In this work, a set of shape contour and color contours are extracted from each grayscale image for object recognition. Taking use of the shape information and the color information, the proposed method enhances the differences between recognition targets and improves the recognition efficiency. Among these, a color contour extracting method was proposed by removing the adjacent points from the binary image. It can keep the color information from the image while reducing the pixels from the binary image.
(4) Adjusting the threshold parameters and identify parameters. In this work, new methods are proposed to help choosing the threshold parameters and identify parameters. A Distance Threshold Determing (DTD) method is used in determining the distance thresholds. By calculating the determining parameters, the DTD method could reduce the scope of values for distance threshold choosing. Meanwhile, a set of network parameters based on node degree is used as recognition parameters. In this condition, more topological characteristics of the network model are extracted for recognition. The complexity of calculating is also reduced.
Experiments show that the proposed method could effectively control the scale of the complex network and adapt to the changes in the boundary shape with efficient power of object recognition. The data from the experiments shows that the proposed method is scale invariant and rotation invariant. The DTD method is also proved to be effective.
This work was supported by the National Program Foundation (61273219), National Natural Science Foundation of Guangdong Province (No.8151009001000061), and Natural Science Joint Research Program Foundation of Guangdong Province (No.8351009001000002).
复杂网络是复杂系统理论中的重要研究对象,复杂网络理论研究受到越来越多研究者的重视,相关的概念和方法都是当前的研究热点。复杂网络利用数学图论建立模型,仅考虑节点间的相对位置等拓扑关系,对网络节点间的顺序关系、节点所处位置等关注较少,网络图的整体旋转、平移等对复杂网络拓扑特性没有影响。因此,如果能够利用复杂网络作为平面图像边界形状的描述模型,并在此基础上建立形状识别算法,那么这种算法将能够有效地适应图像边界形状的改变。
本文在现有相关研究基础上,将复杂网络方法应用于图像目标识别领域,针对形状轮廓识别和灰度图像识别等应用环境,提出一类基于复杂网络的图像目标识别方法。通过将基于轮廓的图像识别方法与复杂网络方法优点相融合,本文方法既保留基于轮廓的识别方法所具有的过程简单、识别效率高等特点,又充分发挥复杂网络方法仅考虑网络拓扑结构,与节点位置、顺序无关等特点,克服了图像轮廓轻微变化、光照强度小幅度变化以及局部遮挡等因素对识别方法的影响,使得识别方法具有平面旋转不变性、平移不变性、缩放不变性以及一定程度的容噪性。
本文研究内容属于二维序列图像中的目标识别。通过获取目标的轮廓,对运动目标进行识别,目标行为的跟踪、分析、描述和理解提供可靠的数据支持。本文方法的主要技术路线是首先对待识别的图像提取形状轮廓和灰度轮廓组,分别保留图像的形状特征和颜色特征。然后将上述轮廓以图的形式表示,利用复杂网络理论建立相应的网络模型,并计算与复杂网络相关的参数。最后通过对所有网络模型提取特征参数,汇集形成识别参数,产生图像目标识别算法用于对象目标的识别和分类。
本文研究内容的创新性主要体现在以下几个方面：
(1)融合复杂网络与轮廓识别方法。利用复杂网络方法抽取目标的轮廓拓扑信息,形成识别参数,将复杂网络方法的优点融入到基于轮廓的目标识别方法中,简化目标网络模型的复杂程度,增强识别方法的容噪性,形成一种有效的目标识别方法。
(2)控制复杂网络规模。本文通过利用图像轮廓、使用简单网络参数、改进建模步骤等方式,从多个方面控制复杂网络模型的规模,减少识别方法占用的存储空间,缩短计算时间。在更简单的网络模型基础上,获取尽可能多的拓扑信息用于目标识别。
(3)改进图像轮廓提取方法。本文提出针对灰度图像分别提取形状轮廓、灰度轮廓用于复杂网络目标识别的新思路,综合利用灰度图像中目标对象的形状特征、色彩特征,提升目标间的区别度,提升识别效率。其中,在灰度轮廓提取方面,本文提出一种使用简单的二值化图像去除邻接点的轮廓提取方法,既克服了二值化图像像素点多的缺点,又在一定程度上保留了图像的色彩信息。
(4)调整阈值参数和识别参数。本文在阈值参数和识别参数选取方面做了改进。提出一种距离阈值判定(DTD)方法,利用样本组的轮廓计算判定参数,用于协助确定距离阈值的取值范围,减少主观判断造成的识别效果不确定性。选择识别参数时,使用了基于节点度的一系列简单的网络参数,通过一次计算得到一组参数,既反映了更丰富的网络模型的拓扑特征,也减少了计算量。
仿真实验数据证明本文方法具有对轮廓图精确度依赖性较低、复杂网络规模小、阈值参数少、能有效适应边界形状改变等优点,在给定的形状轮廓识别、灰度图像识别应用方面具有较高的准确率。本文提出的DTD方法在协助确定距离阈值方面具有有效性。
本文受国家基金项目(61273219)、广东省自然科学基金项目(8151009001000061)、广东省自然科学基金团队项目(8351009001000002)等资助。
Image Object Recognition technology is one of the most important areas of Artificial Intelligence research. It had lots of successful applications such as Video Monitoring, Human-Computer Interaction, Traffic Monitoring, Behavior Recognition, Automatic Navigation, etc. There are several valuable methods for object recognition. According to the features of target object which are used in the recognition process, the object recognition methods can be roughly divided into four categories, such as the Region-Based algorithm, the Contour-Based algorithm, the Model-Based algorithm and the Feature-Based algorithm. The algorithms which are mainly used in object recognition methods can be divided into the Filtering theory, the Mean Shift method, the Partial Differential Equations method, etc. It should be noted that these recognition methods are closely related to the location and sequence of the pixels in the image. So the recognition rate will be reduced by rotation, translational and scaling. In the practical application, the slight changes of the image contour, small variations of the light intensity as well as partial occlusion and other factors will produce adverse effects on the recognition results.
Getting more and more attention by the researchers, Complex Network theory becomes important in the research of the Complex System. The concepts and methods of it have become research focus recently. Modeling by the mathematical Graph theory,Complex Network theory considers only the topological relationships, such as the relative position between nodes. It pays less attention on the location and sequence of the nodes. The overall rotation and translation will not affect the topology of the network diagram. So, a shape recognition algorithm, which uses the Complex Network theory as the description model to the shape of image boundary, may be able to effectively adapt to the changes in the image boundary.
In this work, the Complex Network theory was used in the Image Object Recognition. Based on the existing researches, an image object recognition method based on Complex Network was applied to the shape contour recognition and gray image recognition applications. The proposed method could have the advantages of both Contour-Based recognition method and Complex Network theory, including simple process, high identify efficiency and regardless of the position or order of the nodes in the image. The proposed method can overcome the slight changes of the image contours and the light intensity as well as partial occlusion. In this way, the recognition method is rotation invariance, translation invariance,scale invariance and noise tolerance.
This work takes the Image Object Recognition method in the two-dimensional image sequences into account. By extracting contours from the object, the proposed method recognizes the object from the image sequences. This work will provide reliable data for target tracking, behavior analysis, description and understanding. The main technical route of the proposed method could be described as follow. Firstly, the shape contour and color contours are extracted separately from the image object. Then, the contours are described into graphs and modeled by the Complex Network methodology. Finally, the characteristic parameters are calculated for each network model. And a feature vector for object recognition is extracted for image object recognition and classification.
This work mainly includes the following aspects:
(1) Combining the Complex Network theory and Contour Recognition method together. This work extracts the target identification parameters by the topology information of the contour with Complex Network theory. In this way, the method has the advantages of the Complex Network theory and the Contour Recognition method. With a simpler network model, the proposed method becomes noise tolerance and reaches effective recognition result.
(2) Controlling the complexity of the network. In this work, several methods are used to control the size of the complex network model. Firstly, the proposed method reduces the number of nodes in the network model by extracting the contours from the image. Secondly, the proposed method uses the simpler network parameters in order to reducing the complexity of calculating. Thirdly, the process of modeling is improved. By these changes, the storage space occupied by the recognition method is reduced and the calculation time is shortened. Topology information, as much as possible, is extracted from these simpler network models for object recognition.
(3) Improving the image contour extraction method. In this work, a set of shape contour and color contours are extracted from each grayscale image for object recognition. Taking use of the shape information and the color information, the proposed method enhances the differences between recognition targets and improves the recognition efficiency. Among these, a color contour extracting method was proposed by removing the adjacent points from the binary image. It can keep the color information from the image while reducing the pixels from the binary image.
(4) Adjusting the threshold parameters and identify parameters. In this work, new methods are proposed to help choosing the threshold parameters and identify parameters. A Distance Threshold Determing (DTD) method is used in determining the distance thresholds. By calculating the determining parameters, the DTD method could reduce the scope of values for distance threshold choosing. Meanwhile, a set of network parameters based on node degree is used as recognition parameters. In this condition, more topological characteristics of the network model are extracted for recognition. The complexity of calculating is also reduced.
Experiments show that the proposed method could effectively control the scale of the complex network and adapt to the changes in the boundary shape with efficient power of object recognition. The data from the experiments shows that the proposed method is scale invariant and rotation invariant. The DTD method is also proved to be effective.
This work was supported by the National Program Foundation (61273219), National Natural Science Foundation of Guangdong Province (No.8151009001000061), and Natural Science Joint Research Program Foundation of Guangdong Province (No.8351009001000002).
引文
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