基于结构模型的人运动图像语义描述

人运动图像语义的研究是对人运动图像中人体运动行为的一种描述方法,通过其语义来实现图像的识别与检索。该文希望通过对人体运动的几种较为简单的动作语义的研究,来开启对人运动图像语义的全面研究。为了实现该目标,提出了基于模型的人运动图像的语义描述,即模型语义,模型语义具有直观性、推导性和可行性。通过定义基本模型和语义操作规则,建立一个语义的形式描述理论,此模型语义是人运动图像语义全面研究的基础。     

基于语义的图像检索技术研究

本文从图像语义模型、图像语义的描述方法、图像语义的提取方法三方面介绍基于语义的图像检索技术的发展动态,并根据研究现状,进一步分析如何有效地解决“语义鸿沟”问题。     

作基于常识库支持的图像描述和检索系统*

分析了图像检索系统的研究现状,指出了出现语义鸿沟的原因在于系统中缺乏对于实体相互关系的描述,提出了一个四层的图像语义模型,并在此基础上给出了基于常识库和图像实体库的图像描述和检索模型。以图像的颜色、纹理、形状等特征来构造实体的描述信息,并以常识库信息来分析图像场景中的实体构成和关系,从而获得对图像语义信息的识别和理解。     

图像语义的图形化标注和检索研究

基于图像语义进行检索的目的是希望能够更好地从用户的角度出发,查找出与用户理解相一致的图像。针对目前图像语义检索过程中存在的问题,提出一个基于对象的图像语义内容标注模型和检索框架。首先利用分割算法获取图像中的语义对象区域,然后以MPEG-7标准中的语义描述方案为基础,利用图形化结构实现图像语义内容的标注。在检索过程中,用户把查询内容转化为图形化描述结构,通过提取该描述图的不同长度的路径信息形成查询文档,与图像库中的图像语义标注文档进行匹配实现图像检索。实验结果表明,提出的方法能够有效地实现基于语义的图像标注和检索,与全文检索相比,有较高的查全率和查准率。     

语义图像检索研究进展

语义图像检索已成为解决图像简单视觉特征和用户检索丰富语义之间存在的“语义鸿沟”问题的关键。从图像语义描述方式、图像语义抽取方法和语义检索系统设计3个方面对语义图像检索的研究状况进行了分析和研究;讨论了面向对象的图像内容模型和图像语义表示问题;对利用系统知识的提取、根据用户交互的提取和利用外部信息源的语义生成等具有代表性的语义处理方法进行了阐述;介绍了系统设计中用户界面和语义处理的不同方式,最后从对象识别、语义抽取规则、用户检索模型和图像检索性能评价标准4个方面剖析了实现图像语义处理所面临的困难,并提出了一些初步解决思路。     

XML驱动的图像语义检索研究

本文研究面向语义检索的图像内容描述机制。首先提出图像语义检索整体框架,系统采用XML技术,将图像内容层式描述、图像语义对象自动获取、图像语义相似测度等功能模块加以融合,实现语义层面的图像检索。重点对系统框架中与图像内容描述相关的图像特征分层描述模型、空间位置算子定义、语义对象操作等关键技术进行讨论,并定义相应的XML语义描述框架。检索实验结果表明,该方法具有较好的语义检索性能。     

基于外部信息源的WWW图像语义提取研究

HTML文档作为WWW图像的外部信息源和我体，蕴涵了丰富的描述图像内容的文本信息。为了实现基于语义的WWW图像检索，本文提出了一种WWW图像语义表征模型和图像语义词典的建设方法，给出了一种利用图像语义词典从嵌有WWW图像的HTML网页的相关外部文本信息中提取WWW图像语义信息的具体方法和实验结果。     

一种基于多层语义特征的图像理解方法

视觉场景理解包括检测和识别物体、推理被检测物体之间的视觉关系以及使用语句描述图像区域.为了实现对场景图像更全面、更准确的理解,将物体检测、视觉关系检测和图像描述视为场景理解中3种不同语义层次的视觉任务,提出一种基于多层语义特征的图像理解模型,并将这3种不同语义层进行相互连接以共同解决场景理解任务.该模型通过一个信息传递图将物体、关系短语和图像描述的语义特征同时进行迭代和更新,更新后的语义特征被用于分类物体和视觉关系、生成场景图和描述,并引入融合注意力机制以提升描述的准确性.在视觉基因组和COCO数据集上的实验结果表明,所提出的方法在场景图生成和图像描述任务上拥有比现有方法更好的性能.     

图像层次语义描述的初步研究

关于图像情感的研究主要在心理学和工程学两个领域进行.现有的标准刺激材料来源有限,工程学图像情感研究缺乏专用的刺激材料库,更没有建立涉及对象语义层次的刺激材料群,极大地限制了图像情感的研究.通过对现有刺激材料库的比较和确定对象语义描述,对筛选出的刺激图像进行多层语义标注,形成标注文档并建立图像层次语义描述体系.     

基于智能体和本体的WEB图像检索模型

针对基于关键词WEB图像检索中的语义缺失问题,利用本体的方法描述WEB图像的语义特征,构建了基于智能体和语义特征的WEB图像检索模型,该模型以领域Ontology描述WEB图像的语义特征,通过多个Agent模块分工协作,完成满足用户请求的WEB图像检索.并在Corel提供的图像上进行了仿真实验,验证了该模型解决了基于关键词WEB图像检索模型中的语义缺失问题,提高了WEB图像检索速度和准确率.     

图像语义特征的抽取方法及其应用

从高级信息的角度来描述图像语义，建立图像语义的特征矢量空间和语义划分的结构关系，实现图像与语义值的结构表达。为了有效地获取语义特征值表达，给出了图像语义特征空间选择与最小判别方法，构建了底层特征到高层语义的映射结构与计算表达式，并将特征值应用于图像检索。原理方法和实验数据表明该方法对图像检索具有积极意义。     

Automatic Annotation and Retrieval of Images

Although a variety of techniques have been developed for content-based image retrieval (CBIR), automatic image retrieval by semantics still remains a challenging problem. We propose a novel approach for semantics-based image annotation and retrieval. Our approach is based on the monotonic tree model. The branches of the monotonic tree of an image, termed as structural elements, are classified and clustered based on their low level features such as color, spatial location, coarseness, and shape. Each cluster corresponds to some semantic feature. The category keywords indicating the semantic features are automatically annotated to the images. Based on the semantic features extracted from images, high-level (semantics-based) querying and browsing of images can be achieved. We apply our scheme to analyze scenery features. Experiments show that semantic features, such as sky, building, trees, water wave, placid water, and ground, can be effectively retrieved and located in images.     

Interactive semantics

Much research pursues machine intelligence through better representation of semantics. What is semantics? People in different areas view semantics from different facets although it accompanies interaction through civilization. Some researchers believe that humans have some innate structure in mind for processing semantics. Then, what the structure is like? Some argue that humans evolve a structure for processing semantics through constant learning. Then, how the process is like? Humans have invented various symbol systems to represent semantics. Can semantics be accurately represented? Turing machines are good at processing symbols according to algorithms designed by humans, but they are limited in ability to process semantics and to do active interaction. Super computers and high-speed networks do not help solve this issue as they do not have any semantic worldview and cannot reflect themselves. Can future cyber-society have some semantic images that enable machines and individuals (humans and agents) to reflect themselves and interact with each other with knowing social situation through time? This paper concerns these issues in the context of studying an interactive semantics for the future cyber-society. It firstly distinguishes social semantics from natural semantics, and then explores the interactive semantics in the category of social semantics. Interactive semantics consists of an interactive system and its semantic image, which co-evolve and influence each other. The semantic worldview and interactive semantic base are proposed as the semantic basis of interaction. The process of building and explaining semantic image can be based on an evolving structure incorporating adaptive multi-dimensional classification space and self-organized semantic link network. A semantic lens is proposed to enhance the potential of the structure and help individuals build and retrieve semantic images from different facets, abstraction levels and scales through time.     

一种基于稀疏典型性相关分析的图像检索方法

图像语义检索的一个关键问题就是要找到图像底层特征与语义之间的关联,由于文本是表达语义的一种有效手段,因此提出通过研究文本与图像两种模态之间关系来构建反映两者间潜在语义关联的有效模型的思路,基于该模型,可使用自然语言形式(文本语句)来表达检索意图,最终检索到相关图像.该模型基于稀疏典型性相关分析(sparse canonical correlation analysis,简称sparse CCA),按照如下步骤训练得到:首先利用隐语义分析方法构造文本语义空间,然后以视觉词袋(bag of visual words)来表达文本所对应的图像,最后通过Sparse CCA算法找到一个语义相关空间,以实现文本语义与图像视觉单词间的映射.使用稀疏的相关性分析方法可以提高模型可解释性和保证检索结果稳定性.实验结果验证了Sparse CCA方法的有效性,同时也证实了所提出的图像语义检索方法的可行性.     

Agent逻辑和真假子集语义

理性Agent规约的形式框架通常基于信念、愿望和意图逻辑.为了克服现有的信念、愿望和意图逻辑中存在的问题,为非正规模态算子提供一种合适的语义表示.讨论了理性Agent性态的抽象规约中对语义表示的要求以及现有的信念、愿望和意图逻辑中存在的问题.介绍了作者开发的真假子集语义及其在Agent形式化中的应用.他们的框架使意图的有问题的性质无效.并且证明通过对模型的代数结构施加一定的约束,能获得许多希望的性质.最后对真假子集语义进行了分析.这一切表明真假子集语义为非正规模态算子提供了一种合适的语义表示,是对经典的正规模态算子可能世界语义的一个重要发展,是理性Agent性态的逻辑规约的有力工具,可应用于建立新的合适的Agent逻辑系统.     

Comparison of Semantics of Disjunctive Logic Programs Based on Model-Equivalent Reduction

In this paper, it is shown that stable model semantics, perfect model semantics, and partial stable model semantics of disjunctive logic programs have the same expressive power with respect to the polynomial-time model-equivalent reduction. That is, taking perfect model semantics and stable model semantic as an example, any logic program P can be transformed in polynomial time to another logic program P＇ such that perfect models （resp. stable models） of P i-i correspond to stable models （resp. perfect models） of P＇, and the correspondence can be computed also in polynomial time. However, the minimal model semantics has weaker expressiveness than other mentioned semantics, otherwise, the polynomial hierarchy would collapse to NP.     

Non-Standard Semantics for Program Slicing

In this paper we generalize the notion of compositional semantics to cope with transfinite reductions of a transition system. Standard denotational and predicate transformer semantics, even though compositional, provide inadequate models for some known program manipulation techniques. We are interested in the systematic design of extended compositional semantics, observing possible transfinite computations, i.e. computations that may occur after a given number of infinite loops. This generalization is necessary to deal with program manipulation techniques modifying the termination status of programs, such as program slicing. We include the transfinite generalization of semantics in the hierarchy developed in 1997 by P. Cousot, where semantics at different levels of abstraction are related with each other by abstract interpretation. We prove that a specular hierarchy of non-standard semantics modeling transfinite computations of programs can be specifiedin such a way that the standard hierarchy can be derived by abstract interpretation. We prove that non-standard transfinite denotational and predicate transformer semantics can be both systematically derived as solutions of simple abstract domain equations involving the basic operation of reduced power of abstract domains. This allows us to prove the optimality of these semantics, i.e. they are the most abstract semantics in the hierarchy which are compositional and observe respectively the terminating and initial states of transfinite computations, providing an adequate mathematical model for program manipulation.     

A comparative study of two formal semantics of the SIGNAL language

SIGNAL is a part of the synchronous languages family, which are broadly used in the design of safety-critical real-time systems such as avionics, space systems, and nuclear power plants. There exist several semantics for SIGNAL, such as denotational semantics based on traces (called trace semantics), denotational semantics based on tags (called tagged model semantics), operational semantics presented by structural style through an inductive definition of the set of possible transitions, operational semantics defined by synchronous transition systems (STS), etc. However, there is little research about the equivalence between these semantics. In this work, we would like to prove the equivalence between the trace semantics and the tagged model semantics, to get a determined and precise semantics of the SIGNAL language. These two semantics have several different definitions respectively, we select appropriate ones and mechanize them in the Coq platform, the Coq expressions of the abstract syntax of SIGNAL and the two semantics domains, i.e., the trace model and the tagged model, are also given. The distance between these two semantics discourages a direct proof of equivalence. Instead, we transformthem to an intermediate model, which mixes the features of both the trace semantics and the tagged model semantics. Finally, we get a determined and precise semantics of SIGNAL.