一种基于区域特征关联的图像语义标注方法

图像语义的标注需要解决图像高层语义和底层特征间存在的语义鸿沟。采用基于图像分割、并结合图像区域特征抽取的方法,建立图像区域语义与底层特征间的关联,采用基于距离的分类算法,计算区域特征间的相似性,并对具有相同或相近特征的区域的语义采用关联关键字的方法进行区分,用关键字实现图像语义的自动标注。     

Advanced learning resource management system with ontology-based hierarchy semantic model

The requirement for advanced learning resource management system is a hot topic in e-learning, which should support sharing, reusability and interoperability of learning resource. The development of semantic technology has provided opportunities to solve the problems. In e-learning area, there have been many research efforts in this direction. However, such kinds of existing systems have many weaknesses on resources usability and system availability because of various requirements on learning resources sharing, and the complexity of semantic technology itself. This paper proposes a feasible architecture for constructing learning resource sharing system with high availability through adopting semantic technology, and ontology-based hierarchy semantic model and method to construct course ontology are proposed. The approach has been validated with prototype development.     

基于小波特征点的图像检索算法

点匹配技术在基于内容的图像检索中已被广泛使用.提出了一种新的基于小波变换和Barnard算子的彩色特征点检测算法.首先,将彩色图像分解为3个通道并分别对每个通道作小波变换;其次,对一给定的小波系数,通过模极大值可以在高分辨率子图像中找到与之最相关的一个系数,对于第1层小波系数,通过Barnard算子在原始图像数据中查找潜在特征点;最后,通过自适应门限和连续点集约减算法得到最终的特征点.实验表明,该方法提取的特征点具有很强的鲁棒性并能对图像进行有效的表示,可以有效提高图像检所的准确率.     

采用显著区域匹配的图像拼接算法

将视觉关注模型和均值无缝克隆(MVSC)算法相结合,提出了一种采用显著区域匹配的图像拼接算法。新算法通过改进视觉关注模型提取显著区域,同时利用区域匹配技术实现重叠显著区域的匹配,结合MVSC具有良好的图像融合特性,对图像拼接进行了研究。实验结果表明,该算法不仅可以自动准确地提取显著区域,而且提高了图像匹配的精确度,改善了图像拼接的质量。     

Semi-structured document categorization with a semantic kernel

Since a decade, text categorization has become an active field of research in the machine learning community. Most of the approaches are based on the term occurrence frequency. The performance of such surface-based methods can decrease when the texts are too complex, i.e., ambiguous. One alternative is to use the semantic-based approaches to process textual documents according to their meaning. Furthermore, research in text categorization has mainly focused on “flat texts” whereas many documents are now semi-structured and especially under the XML format. In this paper, we propose a semantic kernel for semi-structured biomedical documents. The semantic meanings of words are extracted using the unified medical language system (UMLS) framework. The kernel, with a SVM classifier, has been applied to a text categorization task on a medical corpus of free text documents. The results have shown that the semantic kernel outperforms the linear kernel and the naive Bayes classifier. Moreover, this kernel was ranked in the top 10 of the best algorithms among 44 classification methods at the 2007 Computational Medicine Center (CMC) Medical NLP International Challenge.     

A probabilistic semantic model for image annotation and multi-modal image retrieval

This paper addresses automatic image annotation problem and its application to multi-modal image retrieval. The contribution of our work is three-fold. (1) We propose a probabilistic semantic model in which the visual features and the textual words are connected via a hidden layer which constitutes the semantic concepts to be discovered to explicitly exploit the synergy among the modalities. (2) The association of visual features and textual words is determined in a Bayesian framework such that the confidence of the association can be provided. (3) Extensive evaluation on a large-scale, visually and semantically diverse image collection crawled from Web is reported to evaluate the prototype system based on the model. In the proposed probabilistic model, a hidden concept layer which connects the visual feature and the word layer is discovered by fitting a generative model to the training image and annotation words through an Expectation-Maximization (EM) based iterative learning procedure. The evaluation of the prototype system on 17,000 images and 7736 automatically extracted annotation words from crawled Web pages for multi-modal image retrieval has indicated that the proposed semantic model and the developed Bayesian framework are superior to a state-of-the-art peer system in the literature.     

基于区域GAC模型的二值化水平集图像分割算法

针对测地线主动轮廓(GAC)模型进行了改进,提出了一种基于区域的GAC模型.通过构造基于区域统计信息的符号压力函数取代边界停止函数,有效解决了弱边界目标或离散状边界目标的分割问题.该模型采用二值化水平集方法实现,避免了传统实现方法水平集函数需要重新初始化为符号距离函数,从而导致稳定性差、计算量大、实现复杂等缺点.对不同类型图像的试验结果表明:该算法迭代收敛速度比GAC模型传统实现方法明显加快,且可有效防止边界泄漏,分割效果优于传统GAC模型与C-V模型.     

图像主题区域提取及其在图像检索中的应用

图像主题区域是表达图像主要语义内容的部分.根据图像不同区域的视觉显著性差异特点,利用改进的视觉注意模型,提取图像的主题区域,并划分为不同语义类别.提出基于主题区域的图像检索方法,该方法能按照图像主题区域所属的语义类别,在图像之间有针对性地选择同类别主题区域进行区域相似性比较.实验结果表明,此方法可以更有效地表达图像的主要语义内容,提高图像检索的效率和准确性.     

改进图割的显著性区域检测算法

为快速准确地提取图像中的显著性区域,提出一种改进图割的显著性区域检测算法。采用改进的图割算法对图像进行预分割,将图像分成若干子区域,在此基础上利用区域间对比度计算各区域的显著性值,得到图像的显著图,利用迭代阈值分割算法对图像显著图进行分割处理,通过图像去噪完整有效地提取出显著性区域。实验结果表明,该算法能够准确地提取出图像中的显著性区域,与传统方法相比,使用该算法提取出的显著性区域更完整、准确,提高了效率。     

图像检索中语义映射方法综述

"语义鸿沟"已成为基于内容图像检索的瓶颈,解决这个问题需要建立从图像的低层特征到高层语义的映射.对当前语义映射研究进行了综述,首先给出一个结合语义的图像检索框架,并分析了图像内容的层次模型及图像语义的表示方法;然后根据算法的特点,将现有的语义映射方法和技术分为4大类,重点阐述了各类方法提出的思路、模型.并讨论各自的优势和局限性;最后以图像检索实际应用的需要为依据,提出在图像语义检索相关领域的重要课题和研究方向.     

Discovering a cover set of ARsi with hierarchy from quantitative databases

This paper extends the work on discovering fuzzy association rules with degrees of support and implication (ARsi). The effort is twofold: one is to discover ARsi with hierarchy so as to express more semantics due to the fact that hierarchical relationships usually exist among fuzzy sets associated with the attribute concerned; the other is to generate a “core” set of rules, namely the rule cover set, that are of more interest in a sense that all other rules could be derived by the cover set. Corresponding algorithms for ARsi with hierarchy and the cover set are proposed along with pruning strategies incorporated to improve the computational efficiency. Some data experiments are conducted as well to show the effectiveness of the approach.     

基于非线性扩散滤波的水平集模型MRI分割

基于曲线演化的图像分割模型在分割目标时需要在目标附近人为地构造一条曲线作为初始曲线,在此基础上进行演化得到目标边界.当初始曲线离目标边界较远时,影响模型分割的效率;当初始曲线离目标边界很近时,意味着需要过多的人为操作,这使得其时间效率较低且易出错.为此,在非线性扩散滤波的基础上,给出一种半自动初始曲线构造方法,该方法首先利用AOS算法对图像进行非线性扩散滤波,再利用区域信息快速地得到离目标边界很近的初始曲线.然后构造一种新的基于区域信息的速度函数,由水平集模型对其演化,得到了较好的结果.MRI分割实验表明了方法的有效性.     

Rule induction for hierarchical attributes using a rough set for the selection of a green fleet

Rough set theory (RST) has been the subject of much study and numerous applications in many areas. However, most previous studies on rough sets have focused on finding rules where the decision attribute has a flat, rather than hierarchical structure. In practical applications, attributes are often organized hierarchically to represent general/specific meanings. This paper (1) determines the optimal decision attribute in a hierarchical level-search procedure, level by level, (2) merges the two stages, generating reducts and inducting decision rules, into a one-shot solution that reduces the need for memory space and the computational complexity and (3) uses a revised strength index to identify meaningful reducts and to improve their accuracy. The selection of a green fleet is used to validate the superiority of the proposed approach and its potential benefits to a decision-making process for transportation industry.     

An alternate approach to developing a total celebrity endorser rating model using the analytic hierarchy process

Using the analytic hierarchy process to develop an overall rating of a celebrity is a new approach to enhance procedures such as the Q‐Rating currently in practice. The Q‐rating approach has received criticism on its usefulness because it essentially only considers familiarity and likeability of the celebrity. Using the AHP model approach allows users to input judgments as to how they feel about one celebrity compared to another with a specific set of criteria in mind. The model is an improvement over the Q‐rating system because it includes a more complete set of attributes and, more importantly, has the ability to capture and analyse trade‐offs among those attributes. Another improvement is that as situations change or evolve, the model can be easily updated to capture new information. This allows for celebrities' ratings to change over time and be more accurate given all available information.     

超像素词包模型与SVM分类的图像标注

为了改善基于词包模型与支持向量机(SVM)分类一幅图对应一个标签的单标签分类问题,提出了一种基于超像素词包模型与SVM分类的图像标注算法.将超像素分割结果作为词包模型的基本单元,用词包模型生成的视觉词汇表示超像素区域特征,保留了图像中的同质区域,很好地利用了图像的区域特征.仿真结果表明,该方法能有效改善基于词包模型与SVM分类的单标签分类问题,且分类的准确性有所提高.     

Fuzzy model tuning based on a training set with fuzzy model output values

The paper considers the training of a fuzzy model with the help of a training set with fuzzy model output values. Two ways are proposed for constructing fuzzy rule-based multifactor models that produce fuzzy numbers at their outputs. The problem of tuning such fuzzy models on the basis of a fuzzy training set is formulated, methods of its solution are considered, and relevant examples are presented. Computational experiments showed that training based on fuzzy data improves the modeling accuracy for both crisp and fuzzy test sets. __________ Translated from Kibernetika i Sistemnyi Analiz, No. 3, pp. 26–32, May–June 2007.     

A study on X-FEM in continuum structural optimization using a level set model

In this paper, we implement the extended finite element method (X-FEM) combined with the level set method to solve structural shape and topology optimization problems. Numerical comparisons with the conventional finite element method in a fixed grid show that the X-FEM leads to more accurate results without increasing the mesh density and the degrees of freedom. Furthermore, the mesh in X-FEM is independent of the physical boundary of the design, so there is no need for remeshing during the optimization process. Numerical examples of mean compliance minimization in 2D are studied in regard to efficiency, convergence and accuracy. The results suggest that combining the X-FEM for structural analysis with the level set based boundary representation is a promising approach for continuum structural optimization.     

Selection of computer-integrated manufacturing technologies using a combined analytic hierarchy process and goal programming model

Investment in computer-integrated manufacturing (CIM) is a multi-attribute problem, which includes both qualitative and quantitative factors. In order to select the best set of CIM alternatives among the competing ones it is necessary to make a trade off between the quantative and qualitative factors some of which may conflict. Selection of appropriate CIM alternatives is vital in manufacturing companies’ long-term competitiveness, and it requires development of selection models. The selection model must consider various quantative and qualitative objectives and constraints simultaneously. For example, companies have generally limited funds to invest in new advanced technology. The size of the allocated fund limits the types and number of CIM alternatives a company can select in a given time period. In this article, a combined model of the analytical hierarchy process and goal programming is proposed to consider multiple objectives and constraints simultaneously. A real-world example is provided to illustrate the application of the combined approach.     

Development of a DRAM spots negotiation system using core hierarchy negotiation mechanism

The major factors of the DRAM negotiable transactions system in this research are the concession strategy and the negotiation mechanism of the sellers and buyers. In terms of negotiation mechanism, the core hierarchy model negotiation mechanism makes use of multistage sub-negotiates procedure to do multi-attribute negotiation for the sellers and buyers. The negotiable mechanism can further increase the successful rate of negotiation and the acceptable degree. In terms of concession strategy, research in the Liang and Doong, the concession strategy is constituted by times of negotiation, method of concession and times of persistence. There are less interactive concession for the sellers and buyers in the concession method of the negotiation in the related research. This research is to study the negotiable results that using various combinations of interactive concession strategy and the core hierarchy model negotiation mechanism. Be used as the reference of negotiation for the DRAM to trade on network.