Similarity retrieval of iconic image database

The perception of spatial relationships among objects in a picture is one of the important selection criteria to discriminate and retrieve the images in an iconic image database system. The data structure called 2D string, proposed by Chang et al., is adopted to represent symbolic pictures. The 2D string preserves the objects' spatial knowledge embedded in images. Since spatial relationship is a fuzzy concept, the capability of similarity retrieval for the retrieval by subpicture is essential. In this paper, similarity measure based on 2D string longest common subsequence is defined. The algorithm for similarity retrieval is also proposed. Similarity retrieval provides the iconic image database with the distinguishing function different from a conventional database.     

快速和准确的单色视频文本提取方法

针对大多数视频文本边缘丰富且颜色单一、水平排列的特点,通过基于dcriche边缘的方法迅速确定视频图像中可能包含文本的区域,使用基于颜色的方法从中提取精确的二值文本图像.实验结果表明,该文本提取方法适用于背景复杂的视频图像,且与单纯基于颜色的算法相比,在速度和提取效果方面更具优越性.     

Knowledge-based vector space model for text clustering

This paper presents a new knowledge-based vector space model (VSM) for text clustering. In the new model, semantic relationships between terms (e.g., words or concepts) are included in representing text documents as a set of vectors. The idea is to calculate the dissimilarity between two documents more effectively so that text clustering results can be enhanced. In this paper, the semantic relationship between two terms is defined by the similarity of the two terms. Such similarity is used to re-weight term frequency in the VSM. We consider and study two different similarity measures for computing the semantic relationship between two terms based on two different approaches. The first approach is based on the existing ontologies like WordNet and MeSH. We define a new similarity measure that combines the edge-counting technique, the average distance and the position weighting method to compute the similarity of two terms from an ontology hierarchy. The second approach is to make use of text corpora to construct the relationships between terms and then calculate their semantic similarities. Three clustering algorithms, bisecting k-means, feature weighting k-means and a hierarchical clustering algorithm, have been used to cluster real-world text data represented in the new knowledge-based VSM. The experimental results show that the clustering performance based on the new model was much better than that based on the traditional term-based VSM.     

Image database design based on 9D-SPA representation for spatial relations

Spatial relationships between objects are important features for designing a content-based image retrieval system. We propose a new scheme, called 9D-SPA representation, for encoding the spatial relations in an image. With this representation, important functions of intelligent image database systems such as visualization, browsing, spatial reasoning, iconic indexing, and similarity retrieval can be easily achieved. The capability of discriminating images based on 9D-SPA representation is much more powerful than any spatial representation method based on minimum bounding rectangles or centroids of objects. The similarity measures using 9D-SPA representation provide a wide range of fuzzy matching capability in similarity retrieval to meet different user's requirements. Experimental results showed that our system is very effective in terms of recall and precision. In addition, the 9D-SPA representation can be incorporated into a two-level index structure to help reduce the search space of each query processing. The experimental results also demonstrated that, on average, only 0.1254 percent /spl sim/ 1.6829 percent of symbolic pictures (depending on various degrees of similarity) were accessed per query in an image database containing 50,000 symbolic pictures.     

应用通道增强MSER与CNN的维吾尔文本区域定位

为了准确有效地定位出图像中的维吾尔文本区域,提出了一种基于通道增强最大稳定极值区域（Maximally Stable Extremal Region,MSER）和卷积神经网络（Convolutional Neural Network,CNN）的图像文本区域定位方法。应用通道增强MSER提取候选区域,根据文本特征的启发式规则以及CNN分类结果去除非文本和重复区域,通过区域融合算法得到词级别文本区域,根据该区域的色彩相近程度和空间关系召回遗漏的文本区域,并通过CNN网络对召回的区域分类融合,定位出图像文本区域。实验结果表明,该方法可以准确有效地定位文本区域,具有鲁棒性和应用性。     

基于全文索引与余弦公式医学文本相似性分析

医学文本相似性问题是医学文本挖掘中的重要内容,如何能够快速计算出大数据量下的医学文本的相似性情况是医学文本相似性计算的重点.针对基于传统余弦公式医学文本相似性分析算法在性能上的缺陷,提出了一种基于全文索引技术与余弦公式医学文本相似性分析算法,对医学文本相似性进行分析.采用全文索引技术对医学文本数据相关关键词进行索引,并根据若干关键词在索引中检索出部分数据,从而减少计算复杂度,提高效率.实验表明,该方法比基于传统余弦公式医学文本相似性分析算法具有更优的性能.     

Interacting with VDL-based structured icons on crisis map for emergency coordination: Interactive design and experimental demonstration

Accurate and timely information sharing among multiple participants is one of the crucial factors for collaboration in crisis management. The icon-based map was frequently applied as an effective means for crisis interaction and collaboration. However, former studies only regarded the icons as supplementary visualization components during the whole crisis collaboration process. In this paper, the concept of a structured-icon-relied interactive method was proposed to directly integrate all kinds of crisis and coordination information through icons on the crisis map. Moreover, structured icons are supposed to explicitly show the correlation among crisis information and to improve the interactive experience of information search. The effectiveness of this interactive method was verified by a controlled experiment with three sub-tasks for simulated crisis rescue. The results of the experiment showed that the design of the crisis map based on structured icons had a positive impact on collaborative decision-making and crisis interaction. The dynamic characteristics of the structured icon could represent the temporal and spatial attributes of crisis information and enhance users’ crisis coordination ability. The study aims to enrich the theory of crisis information visualization and interaction based on structured iconic representation. In practice, this research could optimize the design of a crisis collaboration system based on the icon as well as the interaction between people, crisis information, and collaborative system, which in turn provides accurate and timely rescue decisions.     

A Spatial Access-Oriented Implementation of a 3-D GIS Topological Data Model for Urban Entities

3-D analysis in GIS is still one of the most challenging topics for research. With the goal being to model possible movement within the built environment, this paper, therefore, proposes a new approach to handling connectivity relationships among 3-D objects in urban environments in order to implement spatial access analyses in 3-D space. To achieve this goal, this paper introduces a 3-D network data model called the geometric network model (GNM), which has been developed by transforming the combinatorial data model (CDM), representing a connectivity relationship among 3-D objects using a dual graph. For the transformation, this paper presents (1) an O(n ²) algorithm for computing a straight medial axis transformation (MAT), (2) the processes for transforming phenomena from 3-D CDM to 3-D GNM, and (3) spatial access algorithms for the 3-D geometric network based upon the Dijkstra algorithm. Using the reconstructed geometric network generated from the transformations, spatial queries based upon the complex connectivity relationships between 3-D urban entities are implemented using Dijkstra algorithm. Finally, the paper presents the results of an experimental implementation of a 3-D network data model (GNM) using GIS data of an area in downtown Columbus, Ohio.     

An efficient similarity metric for omnidirectional vision sensors

This paper presents an efficient metric for the computation of the similarity among omnidirectional images (image matching). The representation of image appearance is based on feature vectors that include both the chromatic attributes of color sets and their mutual spatial relationships. The proposed metric fits well to robotic navigation using omnidirectional vision sensors, because it has very important properties: it is reflexive, compositional and invariant with respect to image scaling and rotation. The robustness of the metric was repeatedly tested using omnidirectional images for a robot localization task in a real indoor environment.     

基于空间分布差异度的分块彩色图像检索方法

传统的基于颜色直方图的彩色图像检索方法具有计算简单和尺度变化不敏感的优点,但传统的方法普遍存在丢失图像空间信息和特征维数较高的缺点。为克服上述缺陷,提出了一种基于空间分布差异度的分块颜色直方图图像检索新方法。该算法首先将图像进行划分,并计算图像各子块间的相似度;然后,对分块的图像进行空间分布差异度的计算,并由此确定各分块的权值系数;最后,对各子块的相似度进行加权累加从而得到整幅图像的相似度。实验表明,该算法能克服传统方法的弊端,并具有较好的平均查找性能。     

一种基于基元的彩色图像检索方法

结合基元与颜色特征,提出一种基于基元的彩色图像检索算法,该算法首先将彩色图像从RGB颜色空间转换到HSV空间上,并将图像量化为256种颜色,然后定义五种基元类型对图像进行基元分析得到基元图,利用颜色直方图描述基元图的颜色特征,利用改进的直方图相交算法进行相似度度量。实验结果表明：提出的算法能有效地去除背景颜色对图像目标的检索影响,而且较之用灰度边缘检测的边缘代替彩色图像边缘而进行的检索,能更好地反映彩色图像的纹理和边缘特征,具有较高的查准率和查全率。     

基于朴素贝叶斯Web新闻内容的抽取方法

针对网页信息自动抽取问题,提出一种将网页按标记分块并根据朴素贝叶斯理论从中识别新闻正文的方法。该方法将各分块的标记信息、文本相似度以及字长特征作为机器学习的特征属性。为提高标记属性的表征作用,减少相关标记之间的干扰,算法采用χ2检验法来检验标记属性之间以及标记属性与类别之间的相关性并实现属性约减。新闻正文抽取过程中同时考虑正文与非正文分块的后验概率,以提高抽取准确率。实验结果表明,选取适当的参数值,抽取新闻正文的准确率达到85%。      

Genetic algorithm for text clustering using ontology and evaluating the validity of various semantic similarity measures

This paper proposes a self-organized genetic algorithm for text clustering based on ontology method. The common problem in the fields of text clustering is that the document is represented as a bag of words, while the conceptual similarity is ignored. We take advantage of thesaurus-based and corpus-based ontology to overcome this problem. However, the traditional corpus-based method is rather difficult to tackle. A transformed latent semantic indexing (LSI) model which can appropriately capture the associated semantic similarity is proposed and demonstrated as corpus-based ontology in this article. To investigate how ontology methods could be used effectively in text clustering, two hybrid strategies using various similarity measures are implemented. Experiments results show that our method of genetic algorithm in conjunction with the ontology strategy, the combination of the transformed LSI-based measure with the thesaurus-based measure, apparently outperforms that with traditional similarity measures. Our clustering algorithm also efficiently enhances the performance in comparison with standard GA and k-means in the same similarity environments.     

概念相似度在文本模糊聚类中的应用

文本聚类是文本挖掘的一种重要方法.基于形式概念分析和概念相似度,给出一种新的多背景文本模糊聚类方法和模型.该方法不仅考虑了多背景关键词之间的语义关系,而且通过非距离计算得到模糊相似矩阵. 可根据不同要求得到相应的聚类结果,具有较好的灵活性.最后通过示例说明了所给算法的可行性.     

Feature extraction and iconic visualization

We present a conceptual framework and a process model for feature extraction and iconic visualization. The features are regions of interest extracted from a dataset. They are represented by attribute sets, which play a key role in the visualization process. These attribute sets are mapped to icons, or symbolic parametric objects, for visualization. The features provide a compact abstraction of the original data, and the icons are a natural way to visualize them. We present generic techniques to extract features and to calculate attribute sets, and describe a simple but powerful modeling language which was developed to create icons and to link the attributes to the icon parameters. We present illustrative examples of iconic visualization created with the techniques described, showing the effectiveness of this approach     

一种改进的基于颜色-空间特征的图像检索方法

颜色量化是基于颜色图像检索的一个热点。由于量化边界处的颜色具有连续性和相似性,文章提出了一种改进的基于模糊量化的颜色量化方法,以减小量化误差,使量化方法更接近于人的主观视觉感知。基于这种量化方法,提出了一种基于颜色—空间特征的检索算法,采用了一种相似度量方法以利用相同直方图区间内的像素统计与空间信息之间的相关性。实验结果表明该方法具有较高的检索有效性。     

基于SOM神经网和K-均值算法的图像分割

提出了一种基于SOM神经网络和K-均值的图像分割算法。SOM网络将多维数据映射到低维规则网格中,可以有效地用于大型数据的挖掘;而K-均值是一种动态聚类算法,适用于中小型数据的聚类。文中算法利用SOM网络将具有相似特征的象素S点映射到一个2-D神经网上,再根据神经元间的相似性,利用K-均值算法将神经元聚类。文中将该算法用于彩色图像的分割,并给出了经SOM神经网初聚类后,不同K值下神经元聚类对图像分割的结果及与单纯K-均值分割图像进行对比。     

一种基于空间邻接关系的k-means聚类改进算法

空间对象不仅具有非空间的属性特征，而且具有与空间位置、拓扑结构相关的空间特征。利用传统的聚类方法对空间对象进行聚类时，由于没有考虑空间关系，同一类的对象可能出现在空间不相邻的位置。基于空间邻接关系的k-means改进算法将相邻对象的空间邻接关系作为约束条件加以考虑，使聚类结果既反映了属性特征的相似程度，又反映了对象的空间相邻状态，从而可以揭示不同类别对象的空间分布格局，因此其比传统的k-means方法更适合于空间对象的聚类分析。     

Knowledge Discovery in Multiple Spatial Databases

In this paper, a new approach for centralised and distributed learning from spatial heterogeneous databases is proposed. The centralised algorithm consists of a spatial clustering followed by local regression aimed at learning relationships between driving attributes and the target variable inside each region identified through clustering. For distributed learning, similar regions in multiple databases are first discovered by applying a spatial clustering algorithm independently on all sites, and then identifying corresponding clusters on participating sites. Local regression models are built on identified clusters and transferred among the sites for combining the models responsible for identified regions. Extensive experiments on spatial data sets with missing and irrelevant attributes, and with different levels of noise, resulted in a higher prediction accuracy of both centralised and distributed methods, as compared to using global models. In addition, experiments performed indicate that both methods are computationally more efficient than the global approach, due to the smaller data sets used for learning. Furthermore, the accuracy of the distributed method was comparable to the centralised approach, thus providing a viable alternative to moving all data to a central location.