基于刻面分类标识和聚类树的构件检索方法

针对如何从规模庞大的软件构件库中快速且高效地检索出目标构件的问题,提出一种基于刻面分类标识和聚类树的构件检索方法。使用构件标识集合对构件进行刻面分类标识描述,克服了单纯采用刻面分类法对构件进行分类描述和检索时带来的主观因素的影响;引入聚类树的思想,对构件进行基于语义相似度的聚类分析,建立构件聚类树,能有效地缩小检索范围,减少检索构件与构件库中构件比较的次数,提高检索效率。最后在实验中与一般检索方法对比,实验结果表明该方法的构件查准率为88.3%,查全率为93.1%;而且在大规模的构件库中使用时依然有良好的检索效果。     

深度多模态融合服装风格检索

目的 服装检索方法是计算机视觉与自然语言处理领域的研究热点,其包含基于内容与基于文本的两种查询模态。然而传统检索方法通常存在检索效率低的问题,且很少研究关注服装在风格上的相似性。为解决这些问题,本文提出深度多模态融合的服装风格检索方法。方法 提出分层深度哈希检索模型,基于预训练的残差网络ResNet（residual network）进行迁移学习,并把分类层改造成哈希编码层,利用哈希特征进行粗检索,再用图像深层特征进行细检索。设计文本分类语义检索模型,基于LSTM（long short-term memory）设计文本分类网络以提前分类缩小检索范围,再以基于doc2vec提取的文本嵌入语义特征进行检索。同时提出相似风格上下文检索模型,其参考单词相似性来衡量服装风格相似性。最后采用概率驱动的方法量化风格相似性,并以最大化该相似性的结果融合方法作为本文检索方法的最终反馈。结果 在Polyvore数据集上,与原始ResNet模型相比,分层深度哈希检索模型的top5平均检索精度提高11.6%,检索速度提高2.57 s/次。与传统文本分类嵌入模型相比,本文分类语义检索模型的top5查准率提高29.96%,检索速度提高16.53 s/次。结论 提出的深度多模态融合的服装风格检索方法获得检索精度与检索速度的提升,同时进行了相似风格服装的检索使结果更具有多样性。     

A semantic approach to concept lattice-based information retrieval

The volume of available information is growing, especially on the web, and in parallel the questions of the users are changing and becoming harder to satisfy. Thus there is a need for organizing the available information in a meaningful way in order to guide and improve document indexing for information retrieval applications taking into account more complex data such as semantic relations. In this paper we show that Formal Concept Analysis (FCA) and concept lattices provide a suitable and powerful support for such a task. Accordingly, we use FCA to compute a concept lattice, which is considered both a semantic index to organize documents and a search space to model terms. We introduce the notions of cousin concepts and classification-based reasoning for navigating the concept lattice and retrieve relevant information based on the content of concepts. Finally, we detail a real-world experiment and show that the present approach has very good capabilities for semantic indexing and document retrieval.     

Invariants and canonical forms for systems structural and parametric identification

The basic definitions regarding invariant functions and canonical forms for an equivalence relation on a generic set are first recalled.With reference to observable state space models and to the equivalence relation induced by a change of basis it is then shown how the image of a complete set of independent invariants for the considered equivalence relation can be used to parametrize a subset of canonical forms in the given set.Then the set of polynomial input-output models of the type P(z)y(t)=Q(z)u(t) and the equivalence relation induced by the premultiplication of P and Q by a unimodular matrix are considered and canonical forms parametrized by a complete set of independent invariants introduced.Since the two sets of canonical forms share common sets of complete independent invariants, very simple algebraical links between state space and input-output canonical forms can be deduced.The previous results are used to design efficient algorithms solving the problem of the canonical structural and parametric realization and identification of generic input-output sequences generated by a linear, discrete, time-invariant multivariable system.The results obtained in the identification of a real process are then reported.     

Complexity classes of equivalence problems revisited

To determine if two lists of numbers are the same set, we sort both lists and see if we get the same result. The sorted list is a canonical form for the equivalence relation of set equality. Other canonical forms arise in graph isomorphism algorithms. To determine if two graphs are cospectral (have the same eigenvalues), we compute their characteristic polynomials and see if they are equal; the characteristic polynomial is a complete invariant for cospectrality. Finally, an equivalence relation may be decidable in P without either a complete invariant or canonical form. Blass and Gurevich (1984) asked whether these conditions on equivalence relations—having an FP canonical form, having an FP complete invariant, and being in P—are distinct. They showed that this question requires non-relativizing techniques to resolve. We extend their results, and give new connections to probabilistic and quantum computation.     

Equivalence conditions for behaviors and the Kronecker canonical form

In this paper we explore equivalence conditions and invariants for behaviors given in kernel representations. In case the kernel representation is given in terms of a linear matrix pencil, the invariants for strict equivalence are given by the Kronecker canonical form which, in turn, we interpret in geometric control terms. If the behavior is given in a kernel representation by a higher order rectangular polynomial matrix, the natural equivalence concept is behavior equivalence. These notions are closely related to the Morse group that incorporates state space similarity transformations, state feedback, and output injection. A simple canonical form for behavioral equivalence is given that clearly exhibits the reachable and autonomous parts of the behavior. Using polynomial models we also present a unified approach to pencil equivalence that elucidates the close connections between classification problems from linear algebra, geometric control theory, and behavior theory. We also indicate how to derive the invariants under behavior equivalence from the Kronecker invariants.     

Hierarchy as a new data type for qualitative variables

The concept of hierarchy has being explored by the computer science communities during last few decades. Relatively simple hierarchical structures found extensive use in such diverse areas as data modeling, information retrieval, knowledge representation and processing, natural language, pattern recognition, and so on. Recent investigations in information retrieval and data integration have emphasized the use of ontologies and semantic similarity functions as a mechanism for comparing objects that can be retrieved or integrated across heterogeneous repositories. Hierarchies being a simpler, albeit very useful, version of ontologies, can perfectly contribute to model solutions of these problems. Present paper aims to illustrate above thesis by discussing a simple method of information retrieval that uses a hierarchical qualitative data organization. Its main goal is to retrieve objects from any database that are just close to a desired item and control the retrieval process up to a given error, called herein confusion. For doing this, we define a semantic dissimilarity (confusion) between objects to be retrieved as well as introduce a calculus of predicates based on the confusion function.     

A Fuzzy Approach to Classification of Text Documents

This paper discusses the classification problems of text documents. Based on the concept of the proximity degree, the set of words is partitioned into some equivalence classes.Particularly, the concepts of the semantic field and association degree are given in this paper.Based on the above concepts, this paper presents a fuzzy classification approach for document categorization. Furthermore, applying the concept of the entropy of information, the approaches to select key words from the set of words covering the classification of documents and to construct the hierarchical structure of key words are obtained.     

Automatic document classification based on latent semantic analysis

In this paper, the problem of automatic document classification by a set of given topics is considered. The method proposed is based on the use of the latent semantic analysis to retrieve semantic dependencies between words. The classification of document is based on these dependencies. The results of experiments performed on the basis of the standard test data set TREC (Text REtrieval Conference) confirm the attractiveness of this approach. The relatively low computational complexity of this method at the classification stage makes it possible to be applied to the classification of document streams.     

Fast semantic object search and detection for vegetable trading information using Steiner tree

We propose an approach to speed up the semantic object search and detection for vegetable trading information using Steiner Tree. Through analysis, comparing the relevant ontology construction method, we present a set of ontology construction methods based on domain ontology for vegetables transaction information. With Jena2 provides rule-based reasoning engine, More related information could be searched with the help of ontology database and ontology reasoning, query expansion is to achieve sub-vocabulary of user input, the parent class of words, equivalence class of extensions, and use of ontology reasoning to get some hidden information to use of these technologies, we design and implementation of ontology-based semantic vegetables transaction information retrieval system, and through compare to keyword-based matching of large-scale vegetable trading site retrieval systems, the results show that the recall and precision rate of ontology-based information retrieval system much better than keyword-based information retrieval system, and has some practical value.     

图像的语义关联与图像索引

基于内容图像检索的主要挑战在于不断变化的图像检索要求、难于表达的图像内容以及图像表达的数字阵列与通常可以被人类所接受的概念化内容之间的语义鸿沟.提出了一个基于语义关联的图像检索方法,在语义关联的基础上形成一个场景类别的语义表达,以便用户可以将感知上相似的图像组织在一起,形成概念上下文,使得用户可以解释和标记图像而无需给...     

Multimodal Retrieval using Mutual Information based Textual Query Reformulation

Multimodal Retrieval is a well-established approach for image retrieval. Usually, images are accompanied by text caption along with associated documents describing the image. Textual query expansion as a form of enhancing image retrieval is a relatively less explored area. In this paper, we first study the effect of expanding textual query on both image and its associated text retrieval. Our study reveals that judicious expansion of textual query through keyphrase extraction can lead to better results, either in terms of text-retrieval or both image and text-retrieval. To establish this, we use two well-known keyphrase extraction techniques based on tf-idf and KEA. While query expansion results in increased retrieval efficiency, it is imperative that the expansion be semantically justified. So, we propose a graph-based keyphrase extraction model that captures the relatedness between words in terms of both mutual information and relevance feedback. Most of the existing works have stressed on bridging the semantic gap by using textual and visual features, either in combination or individually. The way these text and image features are combined determines the efficacy of any retrieval. For this purpose, we adopt Fisher-LDA to adjudge the appropriate weights for each modality. This provides us with an intelligent decision-making process favoring the feature set to be infused into the final query. Our proposed algorithm is shown to supersede the previously mentioned keyphrase extraction algorithms for query expansion significantly. A rigorous set of experiments performed on ImageCLEF-2011 Wikipedia Retrieval task dataset validates our claim that capturing the semantic relation between words through Mutual Information followed by expansion of a textual query using relevance feedback can simultaneously enhance both text and image retrieval.     

基于Markov网络及laplacian映射的快速相似性检索方法

快速相似性检索技术对于各种信息检索应用都具有很大的意义,其中基于语义哈希的快速相似性检索即是一个合理有效的检索方式,其检索模型能够在保证语义相关的基础上将高维空间中大量相关的文档数据,映射在低维空间中.虽然近年来许多关于语义哈希的研究都表现了不错的实验结果,但是都没有考虑到利用文档集合自身的信息来加强文档间的相关信息.为了有效利用文档自身信息,提出结合强化文档间邻接关系的马尔可夫迁移过程及使用保留局部信息的拉普拉斯映射方法的相似性检索方式.     

Integrating symbolic images into a multimedia database system using classification and abstraction approaches

Symbolic images are composed of a finite set of symbols that have a semantic meaning. Examples of symbolic images include maps (where the semantic meaning of the symbols is given in the legend), engineering drawings, and floor plans. Two approaches for supporting queries on symbolic-image databases that are based on image content are studied. The classification approach preprocesses all symbolic images and attaches a semantic classification and an associated certainty factor to each object that it finds in the image. The abstraction approach describes each object in the symbolic image by using a vector consisting of the values of some of its features (e.g., shape, genus, etc.). The approaches differ in the way in which responses to queries are computed. In the classification approach, images are retrieved on the basis of whether or not they contain objects that have the same classification as the objects in the query. On the other hand, in the abstraction approach, retrieval is on the basis of similarity of feature vector values of these objects. Methods of integrating these two approaches into a relational multimedia database management system so that symbolic images can be stored and retrieved based on their content are described. Schema definitions and indices that support query specifications involving spatial as well as contextual constraints are presented. Spatial constraints may be based on both locational information (e.g., distance) and relational information (e.g., north of). Different strategies for image retrieval for a number of typical queries using these approaches are described. Estimated costs are derived for these strategies. Results are reported of a comparative study of the two approaches in terms of image insertion time, storage space, retrieval accuracy, and retrieval time. Received June 12, 1998 / Accepted October 13, 1998     

图像数据的结构化协同标注与检索

通过分析图像数据对语义信息的检索需求,提出一种表达图像信息的结构化方法,并定义一种根据图像中隐含语义信息来判定相似度的算法。该方法在一定程度上克服了基于内容的图像检索和基于文本的元数据图像检索这2种常用图像检索方法缺乏语义信息匹配上的不足。原型系统根据结构化协同标注中语义信息的丰富程度与图像检索的精确程度,证明了该方法的有效性。     

Solving Third Order Differential Equations with Maximal Symmetry Group

The largest group of Lie symmetries that a third-order ordinary differential equation (ode) may allow has seven parameters. Equations sharing this property belong to a single equivalence class with a canonical representative v ^′′′(u)=0. Due to this simple canonical form, any equation belonging to this equivalence class may be identified in terms of certain constraints for its coefficients. Furthermore a set of equations for the transformation functions to canonical form may be set up for which large classes of solutions may be determined algorithmically. Based on these steps a solution algorithm is described for any equation with this symmetry type which resembles a similar scheme for second order equations with projective symmetry group. Received March 9, 2000; revised June 8, 2000     

Inheritance rules for flexible model retrieval

Convenient model management requires flexible model retrieval. This paper presents a new flexible retrieval approach for mathematical model bases. The approach defines a multi-valued model inheritance relationship among models at a signature level. The inheritance provides a rich semantic information for the retrieval mechanism to refine inexact retrieval requirements. An inheritance rules reasoning system is proposed to enhance the ability and the efficiency of the model retrieval. The interface of the approach includes an SQL-like command, which enables users to retrieve their required models with inexact requirement expressions. The approach has been implemented in a rule-based mathematical model base system RMMBS. Application examples demonstrate the retrieval approach.     

Visual Ontology Construction for Digitized Art Image Retrieval

Current investigations on visual information retrieval are generally content-based methods. The significant difference between similarity in low-level features and similarity in high-level semantic meanings is still a major challenge in the area of image retrieval. In this work, a scheme for constructing visual ontology to retrieve art images is proposed. The proposed ontology describes images in various aspects, including type ＆ style, objects and global perceptual effects. Concepts in the ontology could be automatically derived. Various art image classification methods are employed based on low-level image features. Non-objective semantics are introduced, and how to express these semantics is given. The proposed ontology scheme could make users more naturally find visual information and thus narrows the “semantic gap”. Experimental implementation demonstrates its good potential for retrieving art images in a human-centered manner.