一种基于图像底层特征的对象粒认知方法研究
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摘要
对象非监督发现、分割的应用领域相当广泛,包括图像理解、视频监控、图像和视频编辑、人机交互、图像和视频检索、视频编码以及对象识别等。分析已有的众多应用,发现有三点显得至关重要：一是所发现的对象与真实对象的逼近度,如边缘的吻合性等；二是所设计的应用算法的通用性和扩展能力,由于图像、视频种类繁多,一次性设计出通用性完美的发现、跟踪算法是不可能的,为达到无限地逼近真实对象的目的,相关算法具有持续升级能力和好的移植能力便显得十分必要；三是因众多资源有限的应用环境的出现,例如无线传感网等,为让图像、视频对象的非监督发现和跟踪能用于这些环境,相应的算法得轻便,易于硬化,最好是线性的。本文针对以上问题,以对象非监督提取和跟踪为目标,借鉴心理学的认知模型,基于底层特征构筑了一个对象粒认知平台,并将其用于显著对象的非监督粗糙提取。本文的主要工作包括：
第一,针对复杂运动对象的跟踪失效情形,提出了一种基于颜色阈值和区域融合的方法。运动在采样时间尺度变长、物体本身外型较复杂且动作轨迹不受约束或受遮挡时,其复杂性将增加。此时将导致被连续跟踪帧间的变化增大,均值漂移算法会因Bhattarchayya系数太低而失效。针对这种情况,本文基于快速颜色阈值和区域融合,改进了均值漂移算法。实验证明,改进算法消除了失效。为做到进一步精密,比如让跟踪的边缘贴合于所跟踪的对象等,本文继续作了以下几项工作
第二,为让计算机非监督地获得“感觉”,提出并实现了一种粒截分模型。通过分析已有的图像、视频底层特征的处理和语义挖掘算法,本文归纳出了一种映射关系,即底层特征、知识表示、语义概念对人类认知的感觉、知觉、表象。以此为指导,开始探索一种由感觉到表象的对图像、视频对象的非监督认知方法。做为基础,为让计算机能自主产生对彩图的“感觉”,基于人工智能领域中的长于模拟人类思维解决复杂问题的粒计算理论,提出了一种粒截分模型。首先以粗糙集和商空间理论为指导,构建拓扑信息系统。接下来,将数据视为超立方,建立由基础截分向量和拓扑截分向量构成的截分向量集,以图分别示意基础截分向量和拓扑截分向量的截分效果,提出了概念粒和连通粒两个新概念,给出了粒截分模型的定义。最后,为实现粒截分模型,设计一种单属性分析器,论证相关的定理及引理,详细描述所有概念粒的截分过程,讨论了存在的不足,并通过计算证实了单属性分析器的低复杂度。在超过三百幅各类彩图上做了实验,结果显示：单属性分析器能有效分析所指定的属性,让计算机从彩图中得到“感觉’
第三,为让“感觉”更清晰,提出了一种可适用于任意图像甚至图像碎片的粒标注算法。首先,根据粒截分模型依据所选用的观察概念截分图像,得到相应的概念粒。然后,行压缩指定概念粒或全部概念粒,得到对应的行连通段。最后,给出了实现粒标注的算法—IGL算法,详细描述标注、提取所有连通区(本文称连通粒)的过程,定义可供后续处理应用的开放存贮结构,讨论标注模型向高维的可扩展性。用二值图和彩图分别作验证和比较分析,所得结果表明：所呈标注算法精确、鲁棒,且较之传统标注算法更高速。
第四,考虑到截分所得的“粒”是任意形状、任意分布的连通区域,对以区域为基元作特征提取和分析进行了探索,提出并实现了一种粒相关边缘模型。本文针对现有边缘提取算法不能提取任意形状、任意分布区域的边缘及所提取的边缘缺乏统一模型、与实际对象的相关性不密切等问题,提出一种粒相关边缘模型,并给出相应的实现算法—任意区域边缘提取算法(Arbitrary Region Edge Extraction,简称(?)REE)。粒相关边缘模型兼容于粒计算理论,由拓扑信息系统、概念粒、连通粒及边缘空间等概念组成。AREE算法引入行连通段等定义,给出并证明计算边缘集的定理,随后依据定理在压缩状态下搜索连通粒中的内点,进而完成边缘提取。对比分析和示例实验都证明：本文所提出的算法能精确、快速地提取出各类图像中任意连通粒的边缘。
第五,基于以上构筑的认知平台,提出了一种显著对象非监督粗糙认知算法。算法首先以粒截分模型用双概念分别拓扑划分论域,标注连通粒；依据尺度过滤掉过小拓扑等价类；用拓扑连通强度、拓扑分布密度等计算出拓扑等价类的拓扑显著度；借改进Fisher线性判别算法找到最大跃变点,裁掉拓扑显著度过小的拓扑等价类,得到候选区；以维扫梯度等捕捉拓扑等价类间的渐变模式,完成局部粗糙分割,得到候选对象,更新候选对象的拓扑显著度；再次调用Fisher线性判别算法裁减,如果还剩多个对象,用位权选择最终显著对象。最后,以实验分步验证了算法的执行过程,并与同类三种算法的提取结果作了比较分析,证实了本文算法的直观语义逼近效果。
The application domain of unsupervised discovery and segmentation of objects is quite broad. It includes video coding, video surveillance, image and video editing, image and video retrieval, human computer interaction, image understanding and object recognition, etc. According to the anaylsis of the numerous applications, it was found that there are three concerned issues:(1)the degree of the closeness, by which discovered objects approach the correct objects, such as the degree of agreement between edges and the capability of restraining possible interferences;(2) the generality and the extented capacity of the proposed algorithms—it is often impossible to make an algorithm discover or track objects perfectly at a time due to the fact that there is a great variety of images and videos, so it is necessary that the proposed algorithm have the capability for sustainable upgrade and good transplant so that the correct objects can be discovered and tracked in a closer and closer way;(3) the complexity—the proposed algorithms must be handy, better linear, so that these algorithms are able to be used to discover and track objects under condition of limited resources such as embedded devices and wireless sensor networks which are witnessing a rapid development. Aiming at solving the problems ibid, centering around the tracking of objects in videos and the unsupervised discovering of objects in images, and using psychological cognition model for reference, this dissertation set up a granular cognition platform for the discovering of objects. And then the platform is used in the unsupervised rough extracting of salient objects. The main work of this dissertation is as follows.
This dissertation starts with the study of the algorithm for the tracking of objects moving in complex motion in videos. Three factors contribute to more complexity of motion:longer sampling period, an moving object with complex appearance and nonrestraint movement and occlusion. And then mean shift algorithm loses its target due to too low a Bhattacharyya coefficient. To treat it, mean shift algorithm is improved based on fast color thresholding and region merging in this dissertation. Visual experiments show the effectiveness of the proposed method. To obtain the moving objects more accurately, such as to let the edge of tracking region coincide with that of the real object in videos, this dissertation continues to do a series of studies with granular computing.
This dissertation generalizes a mapping relationship between semantic mining in images&videos and human cognition from the past algorithms for low-level feature analysis and semantic mining in images and videos. The mapping relationship is:low-level features, knowledge representation and semantic concepts in images&videos to sensation, apperception and presentation in human cognition respectively. So before computers may think like human being, they must acquire sensations from images according to the mapping relationship. It's a new idea to make computers be able to obtain sensations from a color image through some unsupervised ways. To let the idea come into true, a granule-based partitioning model, based on granular computing(GrC) which is a new way to simulate human thinking to help solve complicated problems in the field of computational intelligence, is proposed for color image processing. First, on the basis of the analyses for rough sets and quotient space, this dissertation constructs a topology information system. And then, this dissertation deems data a hypercube, creates a partition vector set consisting of a basic cross-section vector and several topology cross-section vectors, illustrates the ocular appearances of partitions obtained with the basic cross-section vector and the topology cross-section vectors respectively, defines two new concepts, attribute granules(AtG) and connected granules(CoG), and presents the definitions of the granule-based model. Finally, in order to fulfill the granule-based model, this dissertation designs a single attribute analyser(SAA), defines some theorems and lemmas, describes the processing of extracting all attibute granules in details, discusses limitations, and presents time complexity analysis which shows SAA is high-speed. Experimental results on over300color images show that the proposed analyser is accurate, robust, and able to provide sensations for computers.
The sensations obtained above are vague, and in order to make these sensations clear and provide follow-up procedure with a scalable and uniform platform, this dissertation makes a deep study of unsuperivised labeling algorithms for images and designs a granular labeling model suitable for any images even fragments of images. Firstly, this dissertation divided images into concept granules with the granule-based partitioning model according to the selected concept. And then, this dissertation compresses the selected concept granules or all concept granules based on runlength coding row by row and acquires the basic units(referred to as runs) of them. Finally, in order to fulfill the granule-based labeling model, this dissertation designs a labeling algorithm, image granule labeling(IGL), describes the processing of labeling and extracting all connected components(referred to as connected granules) in details, defines the open storage structure which can be used by late stage works, and discusses the expandability of the proposed labeling model to high-dimension. Comparisons and experimental results on binary and color images show that the proposed labeling algorithm is accurate and robust, and quicker than conventional labeling algorithms.
Granules used as basic units of the basic platform mentioned above may be connected regions in any shapes or arbitary distributions, which results in a new problem—how to do feature extraction and analysis when basic units are regions. So we makes studies of edge extraction for connected granules. Most of the algorithms for edge extraction are incompetent at edge extraction of arbitrary regions(in any shape or sparse), and without a unified model, the edges obtained by these algorithms are not closely related to the real objects in images. To address these issues, this dissertation construct an edge model related to granules in images based on granular computing. Then the algorithm for arbitrary region edge extraction (referred to as AREE), which is used to fulfill the edge model related to granules in images, is presented. The edge model which is compatible with granular computing consists of four new concepts:topology information system, concept granule, connected granule and edge space. As a way of realizing the edge model, the AREE algorithm introduces some new concepts such as run(a run is a block of contiguous pixels of a concept granule in a row) and interior-point firstly, then presents and proves the theorem used to obtain edges, and finally extracts edges through searching interior-points according to the theorem. The comparative analysis and the experimental results on various types of images show that the proposed algorithm is able to extract edges from arbitrary regions(they may be in any shape or sparse) accurately and quickly.
Finally, this dissertation presents an unsupervised rough cognition algorithm based on the basic platform mentioned above for salient object extraction. Firstly, the universe is partitioned with the granular partitioning model according to two concepts respectively, and connected granules are labeled. Then, the following steps are done successively:(1) topology equivalence classes with smaller scale are filtered;(2)the significance of topology equivalence classes is quantized with topology connectivity and topology distribution density;(3)the cut-off position in the significance sequence is found with the improved Fisher's linear discriminant algorithm, and then candidate regions are obtained by removing non-significant topology equivalence classes;(4)gradual changing pattern is expressed with dimensional scan gradient, which is used to do the local rough segmentation until candidate objects are available, the candidate objects that follow the gradual changing pattern are merged and the significant values of these candidate objects are refreshed;(5)Fisher's linear discriminant algorithm is run again, and the final salient object is determined according to position weight if more than one candidate object is left. Finally, the executing process of the proposed approach is validated by experiment step by step, and a comparative analysis with three recent methods is conducted, which shows the superiority of the proposed approach in terms of ability to approximate semantic of object and speed.
第一,针对复杂运动对象的跟踪失效情形,提出了一种基于颜色阈值和区域融合的方法。运动在采样时间尺度变长、物体本身外型较复杂且动作轨迹不受约束或受遮挡时,其复杂性将增加。此时将导致被连续跟踪帧间的变化增大,均值漂移算法会因Bhattarchayya系数太低而失效。针对这种情况,本文基于快速颜色阈值和区域融合,改进了均值漂移算法。实验证明,改进算法消除了失效。为做到进一步精密,比如让跟踪的边缘贴合于所跟踪的对象等,本文继续作了以下几项工作
第二,为让计算机非监督地获得“感觉”,提出并实现了一种粒截分模型。通过分析已有的图像、视频底层特征的处理和语义挖掘算法,本文归纳出了一种映射关系,即底层特征、知识表示、语义概念对人类认知的感觉、知觉、表象。以此为指导,开始探索一种由感觉到表象的对图像、视频对象的非监督认知方法。做为基础,为让计算机能自主产生对彩图的“感觉”,基于人工智能领域中的长于模拟人类思维解决复杂问题的粒计算理论,提出了一种粒截分模型。首先以粗糙集和商空间理论为指导,构建拓扑信息系统。接下来,将数据视为超立方,建立由基础截分向量和拓扑截分向量构成的截分向量集,以图分别示意基础截分向量和拓扑截分向量的截分效果,提出了概念粒和连通粒两个新概念,给出了粒截分模型的定义。最后,为实现粒截分模型,设计一种单属性分析器,论证相关的定理及引理,详细描述所有概念粒的截分过程,讨论了存在的不足,并通过计算证实了单属性分析器的低复杂度。在超过三百幅各类彩图上做了实验,结果显示：单属性分析器能有效分析所指定的属性,让计算机从彩图中得到“感觉’
第三,为让“感觉”更清晰,提出了一种可适用于任意图像甚至图像碎片的粒标注算法。首先,根据粒截分模型依据所选用的观察概念截分图像,得到相应的概念粒。然后,行压缩指定概念粒或全部概念粒,得到对应的行连通段。最后,给出了实现粒标注的算法—IGL算法,详细描述标注、提取所有连通区(本文称连通粒)的过程,定义可供后续处理应用的开放存贮结构,讨论标注模型向高维的可扩展性。用二值图和彩图分别作验证和比较分析,所得结果表明：所呈标注算法精确、鲁棒,且较之传统标注算法更高速。
第四,考虑到截分所得的“粒”是任意形状、任意分布的连通区域,对以区域为基元作特征提取和分析进行了探索,提出并实现了一种粒相关边缘模型。本文针对现有边缘提取算法不能提取任意形状、任意分布区域的边缘及所提取的边缘缺乏统一模型、与实际对象的相关性不密切等问题,提出一种粒相关边缘模型,并给出相应的实现算法—任意区域边缘提取算法(Arbitrary Region Edge Extraction,简称(?)REE)。粒相关边缘模型兼容于粒计算理论,由拓扑信息系统、概念粒、连通粒及边缘空间等概念组成。AREE算法引入行连通段等定义,给出并证明计算边缘集的定理,随后依据定理在压缩状态下搜索连通粒中的内点,进而完成边缘提取。对比分析和示例实验都证明：本文所提出的算法能精确、快速地提取出各类图像中任意连通粒的边缘。
第五,基于以上构筑的认知平台,提出了一种显著对象非监督粗糙认知算法。算法首先以粒截分模型用双概念分别拓扑划分论域,标注连通粒；依据尺度过滤掉过小拓扑等价类；用拓扑连通强度、拓扑分布密度等计算出拓扑等价类的拓扑显著度；借改进Fisher线性判别算法找到最大跃变点,裁掉拓扑显著度过小的拓扑等价类,得到候选区；以维扫梯度等捕捉拓扑等价类间的渐变模式,完成局部粗糙分割,得到候选对象,更新候选对象的拓扑显著度；再次调用Fisher线性判别算法裁减,如果还剩多个对象,用位权选择最终显著对象。最后,以实验分步验证了算法的执行过程,并与同类三种算法的提取结果作了比较分析,证实了本文算法的直观语义逼近效果。
The application domain of unsupervised discovery and segmentation of objects is quite broad. It includes video coding, video surveillance, image and video editing, image and video retrieval, human computer interaction, image understanding and object recognition, etc. According to the anaylsis of the numerous applications, it was found that there are three concerned issues:(1)the degree of the closeness, by which discovered objects approach the correct objects, such as the degree of agreement between edges and the capability of restraining possible interferences;(2) the generality and the extented capacity of the proposed algorithms—it is often impossible to make an algorithm discover or track objects perfectly at a time due to the fact that there is a great variety of images and videos, so it is necessary that the proposed algorithm have the capability for sustainable upgrade and good transplant so that the correct objects can be discovered and tracked in a closer and closer way;(3) the complexity—the proposed algorithms must be handy, better linear, so that these algorithms are able to be used to discover and track objects under condition of limited resources such as embedded devices and wireless sensor networks which are witnessing a rapid development. Aiming at solving the problems ibid, centering around the tracking of objects in videos and the unsupervised discovering of objects in images, and using psychological cognition model for reference, this dissertation set up a granular cognition platform for the discovering of objects. And then the platform is used in the unsupervised rough extracting of salient objects. The main work of this dissertation is as follows.
This dissertation starts with the study of the algorithm for the tracking of objects moving in complex motion in videos. Three factors contribute to more complexity of motion:longer sampling period, an moving object with complex appearance and nonrestraint movement and occlusion. And then mean shift algorithm loses its target due to too low a Bhattacharyya coefficient. To treat it, mean shift algorithm is improved based on fast color thresholding and region merging in this dissertation. Visual experiments show the effectiveness of the proposed method. To obtain the moving objects more accurately, such as to let the edge of tracking region coincide with that of the real object in videos, this dissertation continues to do a series of studies with granular computing.
This dissertation generalizes a mapping relationship between semantic mining in images&videos and human cognition from the past algorithms for low-level feature analysis and semantic mining in images and videos. The mapping relationship is:low-level features, knowledge representation and semantic concepts in images&videos to sensation, apperception and presentation in human cognition respectively. So before computers may think like human being, they must acquire sensations from images according to the mapping relationship. It's a new idea to make computers be able to obtain sensations from a color image through some unsupervised ways. To let the idea come into true, a granule-based partitioning model, based on granular computing(GrC) which is a new way to simulate human thinking to help solve complicated problems in the field of computational intelligence, is proposed for color image processing. First, on the basis of the analyses for rough sets and quotient space, this dissertation constructs a topology information system. And then, this dissertation deems data a hypercube, creates a partition vector set consisting of a basic cross-section vector and several topology cross-section vectors, illustrates the ocular appearances of partitions obtained with the basic cross-section vector and the topology cross-section vectors respectively, defines two new concepts, attribute granules(AtG) and connected granules(CoG), and presents the definitions of the granule-based model. Finally, in order to fulfill the granule-based model, this dissertation designs a single attribute analyser(SAA), defines some theorems and lemmas, describes the processing of extracting all attibute granules in details, discusses limitations, and presents time complexity analysis which shows SAA is high-speed. Experimental results on over300color images show that the proposed analyser is accurate, robust, and able to provide sensations for computers.
The sensations obtained above are vague, and in order to make these sensations clear and provide follow-up procedure with a scalable and uniform platform, this dissertation makes a deep study of unsuperivised labeling algorithms for images and designs a granular labeling model suitable for any images even fragments of images. Firstly, this dissertation divided images into concept granules with the granule-based partitioning model according to the selected concept. And then, this dissertation compresses the selected concept granules or all concept granules based on runlength coding row by row and acquires the basic units(referred to as runs) of them. Finally, in order to fulfill the granule-based labeling model, this dissertation designs a labeling algorithm, image granule labeling(IGL), describes the processing of labeling and extracting all connected components(referred to as connected granules) in details, defines the open storage structure which can be used by late stage works, and discusses the expandability of the proposed labeling model to high-dimension. Comparisons and experimental results on binary and color images show that the proposed labeling algorithm is accurate and robust, and quicker than conventional labeling algorithms.
Granules used as basic units of the basic platform mentioned above may be connected regions in any shapes or arbitary distributions, which results in a new problem—how to do feature extraction and analysis when basic units are regions. So we makes studies of edge extraction for connected granules. Most of the algorithms for edge extraction are incompetent at edge extraction of arbitrary regions(in any shape or sparse), and without a unified model, the edges obtained by these algorithms are not closely related to the real objects in images. To address these issues, this dissertation construct an edge model related to granules in images based on granular computing. Then the algorithm for arbitrary region edge extraction (referred to as AREE), which is used to fulfill the edge model related to granules in images, is presented. The edge model which is compatible with granular computing consists of four new concepts:topology information system, concept granule, connected granule and edge space. As a way of realizing the edge model, the AREE algorithm introduces some new concepts such as run(a run is a block of contiguous pixels of a concept granule in a row) and interior-point firstly, then presents and proves the theorem used to obtain edges, and finally extracts edges through searching interior-points according to the theorem. The comparative analysis and the experimental results on various types of images show that the proposed algorithm is able to extract edges from arbitrary regions(they may be in any shape or sparse) accurately and quickly.
Finally, this dissertation presents an unsupervised rough cognition algorithm based on the basic platform mentioned above for salient object extraction. Firstly, the universe is partitioned with the granular partitioning model according to two concepts respectively, and connected granules are labeled. Then, the following steps are done successively:(1) topology equivalence classes with smaller scale are filtered;(2)the significance of topology equivalence classes is quantized with topology connectivity and topology distribution density;(3)the cut-off position in the significance sequence is found with the improved Fisher's linear discriminant algorithm, and then candidate regions are obtained by removing non-significant topology equivalence classes;(4)gradual changing pattern is expressed with dimensional scan gradient, which is used to do the local rough segmentation until candidate objects are available, the candidate objects that follow the gradual changing pattern are merged and the significant values of these candidate objects are refreshed;(5)Fisher's linear discriminant algorithm is run again, and the final salient object is determined according to position weight if more than one candidate object is left. Finally, the executing process of the proposed approach is validated by experiment step by step, and a comparative analysis with three recent methods is conducted, which shows the superiority of the proposed approach in terms of ability to approximate semantic of object and speed.
引文
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