Active learning based on maximizing information gain for content-based image retrieval

This paper describes a new method for active learning in content-based image retrieval. The proposed method firstly uses support vector machine (SVM) classifiers to learn an initial query concept. Then the proposed active learning scheme employs similarity measure to check the current version space and selects images with maximum expected information gain to solicit user's label. Finally, the learned query is refined based on the user's further feedback. With the combination of SVM classifier and similarity measure, the proposed method can alleviate model bias existing in each of them. Our experiments on several query concepts show that the proposed method can learn the user's query concept quickly and effectively only with several iterations.

Orbit Weighting Scheme in the Context of Vector Space Information Retrieval

This study introduces the Orbit Weighting Scheme(OWS),a novel approach aimed at enhancing the precision and efficiency of Vector Space information retrieval(IR)models,which have traditionally relied on weighting schemes like tf-idf and BM25.These conventional methods often struggle with accurately capturing document relevance,leading to inefficiencies in both retrieval performance and index size management.OWS proposes a dynamic weighting mechanism that evaluates the significance of terms based on their orbital position within the vector space,emphasizing term relationships and distribution patterns overlooked by existing models.Our research focuses on evaluating OWS’s impact on model accuracy using Information Retrieval metrics like Recall,Precision,InterpolatedAverage Precision(IAP),andMeanAverage Precision(MAP).Additionally,we assessOWS’s effectiveness in reducing the inverted index size,crucial for model efficiency.We compare OWS-based retrieval models against others using different schemes,including tf-idf variations and BM25Delta.Results reveal OWS’s superiority,achieving a 54%Recall and 81%MAP,and a notable 38%reduction in the inverted index size.This highlights OWS’s potential in optimizing retrieval processes and underscores the need for further research in this underrepresented area to fully leverage OWS’s capabilities in information retrieval methodologies.

Database Search Behaviors: Insight from a Survey of Information Retrieval Practices

This study examines the database search behaviors of individuals, focusing on gender differences and the impact of planning habits on information retrieval. Data were collected from a survey of 198 respondents, categorized by their discipline, schooling background, internet usage, and information retrieval preferences. Key findings indicate that females are more likely to plan their searches in advance and prefer structured methods of information retrieval, such as using library portals and leading university websites. Males, however, tend to use web search engines and self-archiving methods more frequently. This analysis provides valuable insights for educational institutions and libraries to optimize their resources and services based on user behavior patterns.

The Preliminary Design and Implement of Plant Digital Information Retrieval System

[Objective] The aim was to set up a plant digital information retrieval system.[Method] Plant digital information retrieval system was designed by combining with Microsoft Visual Basic 6.0 Enterprise Edition database management system and Structure Query Language.[Result] The system realized electronic management and retrieval of local plant information.The key words of retrieval included family,genus,formal name,Chinese name,Latin,morphological characteristics,habitat,collection people,collection places,and protect class and so on.[Conclusion] It provided reference for these problems of species identification and digital management of herbarium.

Query Expansion for Chinese Information Retrieval by Using a Decaying Co-occurrence Model

Query expansion with thesaurus is one of the useful techniques in modern information retrieval (IR). In this paper, a method of query expansion for Chinese IR by using a decaying co-occurrence model is proposed and realized. The model is an extension of the traditional co-occurrence model by adding a decaying factor that decreases the mutual information when the distance between the terms increases. Experimental results on TREC-9 collections show this query expansion method results in significant improvements over the IR without query expansion.

Concept-based approach for information retrieval

A concept-based approach is expected to resolve the word sense ambiguities in information retrieval and apply the semantic importance of the concepts, instead of the term frequency, to representing the contents of a document. Consequently, a formalized document framework is proposed. The document framework is used to express the meaning of a document with the concepts which are expressed by high semantic importance. The framework consists of two parts： the ＂domain＂ information and the ＂situation ＆ background＂ information of a document. A document-extracting algorithm and a two-stage smoothing method are also proposed. The quantification of the similarity between the query and the document framework depends on the smoothing method. The experiments on the TREC6 collection demonstrate the feasibility and effectiveness of the proposed approach in information retrieval tasks. The average recall level precision of the model using the proposed approach is about 10% higher than that of traditional ones.

Agriculture Information Retrieval System Based on Comprehensive Information Theory

Through analyzing syntactic,semantic,pragmatic information,the retrieval system ACIS based on comprehensive information was established,which could achieve personalized information exaction to guide user s information retrieval.

Auto-expanded multi query examples technology in content-based image retrieval

In order to narrow the semantic gap existing in content-based image retrieval （CBIR）,a novel retrieval technology called auto-extended multi query examples （AMQE） is proposed.It expands the single one query image used in traditional image retrieval into multi query examples so as to include more image features related with semantics.Retrieving images for each of the multi query examples and integrating the retrieval results,more relevant images can be obtained.The property of the recall-precision curve of a general retrieval algorithm and the K-means clustering method are used to realize the expansion according to the distance of image features of the initially retrieved images.The experimental results demonstrate that the AMQE technology can greatly improve the recall and precision of the original algorithms.

A Multi-Tuple Rough Set Approach for Information Retrieval

How to deal with the imprecise information retrieval has become more and more important in the present information society. An efficient and effective method of information retrieval based on multi tuple rough set is discussed in this paper. The new approach is considered as a generalization of the original rough set model for flexible information retrieval. The imprecise query results can be obtained by multi tuple approximations.

Web Information Retrieval的分析与展望

网络的飞速发展使得网络信息检索成为人们迫切的需求。本文对网络信息检索技术进行了介绍、分析和比较,在此基础上,对未来的应用前景进行了展望。

An Ontology-Based Information Retrieval Model for Vegetables E-Commerce

With the rapid increment of the information on the web, traditional information retrieval based on the keywords is far from user＇s satisfaction in recall and precision. In order to improve the recall ratio and the precision radio of IR engine in the vegetables e-commerce, an information retrieval model based on the vegetables e-commerce ontology is presented in this paper, vegetables e-commerce ontology was constructed by gathering and the analyzing vegetables e-commerce domain information on the web. The vegetables e-commerce ontology is composed of some kinds of vegetable classes and hierarchy relationship of vegetables classes. In the process of information retrieval, domain ontology helps to index information and information inference. An ontology-based information retrieval model is implemented, and which has more functions than the keyword-based web information retrieval engines. The experiment results show that the recall ratio and the precision ratio of ontology-based information retrieval model are higher than that of the information retrieval engine based on keyword at a certain extent.

An Efficient Content-Based Image Retrieval System Using kNN and Fuzzy Mathematical Algorithm

The implementation of content-based image retrieval(CBIR)mainly depends on two key technologies:image feature extraction and image feature matching.In this paper,we extract the color features based on Global Color Histogram(GCH)and texture features based on Gray Level Co-occurrence Matrix(GLCM).In order to obtain the effective and representative features of the image,we adopt the fuzzy mathematical algorithm in the process of color feature extraction and texture feature extraction respectively.And we combine the fuzzy color feature vector with the fuzzy texture feature vector to form the comprehensive fuzzy feature vector of the image according to a certain way.Image feature matching mainly depends on the similarity between two image feature vectors.In this paper,we propose a novel similarity measure method based on k-Nearest Neighbors(kNN)and fuzzy mathematical algorithm(SBkNNF).Finding out the k nearest neighborhood images of the query image from the image data set according to an appropriate similarity measure method.Using the k similarity values between the query image and its k neighborhood images to constitute the new k-dimensional fuzzy feature vector corresponding to the query image.And using the k similarity values between the retrieved image and the k neighborhood images of the query image to constitute the new k-dimensional fuzzy feature vector corresponding to the retrieved image.Calculating the similarity between the two kdimensional fuzzy feature vector according to a certain fuzzy similarity algorithm to measure the similarity between the query image and the retrieved image.Extensive experiments are carried out on three data sets:WANG data set,Corel-5k data set and Corel-10k data set.The experimental results show that the outperforming retrieval performance of our proposed CBIR system with the other CBIR systems.

Hybrid Chinese Information Retrieval Model Based on the Combination of Keyword and Concept

A hybrid model that is based on the Combination of keywords and concept was put forward. The hybrid model is built on vector space model and probabilistic reasoning network. It not only can exert the advantages of keywords retrieval and concept retrieval but also can compensate for their shortcomings. Their parameters can be adjusted according to different usage in order to accept the best information retrieval result, and it has been proved by our experiments.

Research and Application on Web Information Retrieval Based on Improved FP-Growth Algorithm

A kind of single linked lists named aggregative chain is introduced to the algorithm, thus improving the architecture of FP tree. The new FP tree is a one-way tree and only the pointers that point its parent at each node are kept. Route information of different nodes in a same item are compressed into aggregative chains so that the frequent patterns will be produced in aggregative chains without generating node links and conditional pattern bases. An example of Web key words retrieval is given to analyze and verify the frequent pattern algorithm in this paper.

New Approach on the Techniques of Content-Based Image Retrieval (CBIR) Using Color, Texture and Shape Features

Digital image collection as rapidly increased along with the development of computer network. Image retrieval system was developed purposely to provide an efficient tool for a set of images from a collection of images in the database that matches the user’s requirements in similarity evaluations such as image content similarity, edge, and color similarity. Retrieving images based on the content which is color, texture, and shape is called content based image retrieval (CBIR). The content is actually the feature of an image and these features are extracted and used as the basis for a similarity check between images. The algorithms used to calculate the similarity between extracted features. There are two kinds of content based image retrieval which are general image retrieval and application specific image retrieval. For the general image retrieval, the goal of the query is to obtain images with the same object as the query. Such CBIR imitates web search engines for images rather than for text. For application specific, the purpose tries to match a query image to a collection of images of a specific type such as fingerprints image and x-ray. In this paper, the general architecture, various functional components, and techniques of CBIR system are discussed. CBIR techniques discussed in this paper are categorized as CBIR using color, CBIR using texture, and CBIR using shape features. This paper also describe about the comparison study about color features, texture features, shape features, and combined features (hybrid techniques) in terms of several parameters. The parameters are precision, recall and response time.

Content-based Image Retrieval Using Color Histogram

This paper introduces the principles of using color histogram to match images in CBIR. And a prototype CBIR system is designed with color matching function. A new method using 2-dimensional color histogram based on hue and saturation to extract and represent color information of an image is presented. We also improve the Euclidean-distance algorithm by adding Center of Color to it. The experiment shows modifications made to Euclidean-distance signif-icantly elevate the quality and efficiency of retrieval.

Intelligent Agent-Based System for Digital Library Information Retrieval

A new information search model is reported and the design and implementation of a system based on intelligent agent is presented. The system is an assistant information retrieval system which helps users to search what they need. The system consists of four main components: interface agent, information retrieval agent, broker agent and learning agent. They collaborate to implement system functions. The agents apply learning mechanisms based on an improved ID3 algorithm.

A new method for information retrieval in two-dimensional grating-based X-ray phase contrast imaging

Grating-based X-ray phase contrast imaging has been demonstrated to he an extremely powerful phase-sensitive imaging technique. By using two-dimensional （2D） gratings, the observable contrast is extended to two refraction directions. Recently, we have developed a novel reverse-projection （RP） method, which is capable of retrieving the object information efficiently with one-dimensional （1D） grating-based phase contrast imaging. In this contribution, we present its extension to the 2D grating-based X-ray phase contrast imaging, named the two-dimensional reverse- projection （2D-RP） method, for information retrieval. The method takes into account the nonlinear contributions of two refraction directions and allows the retrieval of the absorption, the horizontal and the vertical refraction images. The obtained information can be used for the reconstruction of the three-dimensionak phase gradient field, and for an improved phase map retrieval and reconstruction. Numerical experiments are carried out, and the results confirm the validity of the 2D-RP method.

Optimization in the Migration Problem of Mobile Agents in Distributed Information Retrieval Systems

In this paper, we employ genetic algorithms to solve the migration problem (MP). We propose a new encoding scheme to represent trees, which is composed of two parts: the pre-ordered traversal sequence of tree vertices and the children number sequence of corresponding tree vertices. The proposed encoding scheme has the advantages of simplicity for encoding and decoding, ease for GA operations, and better equilibrium between exploration and exploitation. It is also adaptive in that, with few restrictions on the length of code, it can be freely lengthened or shortened according to the characteristics of the problem space. Furthermore, the encoding scheme is highly applicable to the degree-constrained minimum spanning tree problem because it also contains the degree information of each node. The simulation results demonstrate the higher performance of our algorithm, with fast convergence to the optima or sub-optima on various problem sizes. Comparing with the binary string encoding of vertices, when the problem size is large, our algorithm runs remarkably faster with comparable search capability. Key words distributed information retrieval - mobile agents - migration problem - genetic algorithms CLC number TP 301. 6 Foundation item: Supported by the National Natural Science Foundation of China (90104005), the Natural Science Foundation of Hubei Province and the Hong Kong Polytechnic University under the grant G-YD63Biography: He Yan-xiang (1952-), male, Professor, research direction: distributed and parallel processing, multi-agent systems, data mining and e-business.

Design of Content-Based Retrieval System in Remote Sensing Image Database

To retrieve the object region efficaciously from massive remote sensing image database, a model for content-based retrieval of remote sensing image is given according to the characters of remote sensing image application firstly, and then the algorithm adopted for feature extraction and multidimensional indexing, and relevance feedback by this model are analyzed in detail. Finally, the contents intending to be researched about this model are proposed.