Efficient instance retrieval with standard and relational path indexing

Backward demodulation is a simplification technique used in saturation-based theorem proving with superposition and ordered paramodulation. It requires instance retrieval, i.e., search for instances of some term in a typically large set of terms. Path indexing is a family of indexing techniques that can be used to solve this problem efficiently. We propose a number of powerful optimisations to standard path indexing. We also describe a novel framework that combines path indexing with relational joins. The main advantage of the proposed scheme is flexibility, which we illustrate by sketching how to adapt the scheme to instance retrieval modulo commutativity and backward subsumption on multi-literal clauses.     

Fast Term Indexing with Coded Context Trees

Indexing data structures have a crucial impact on the performance of automated theorem provers. Examples are discrimination trees, which are like tries where terms are seen as strings and common prefixes are shared, and substitution trees, where terms keep their tree structure and all common contexts can be shared. Here we describe a new indexing data structure, called context trees, where, by means of a limited kind of context variables, common subterms also can be shared, even if they occur below different function symbols. Apart from introducing the concept, we also provide evidence for its practical value. We show how context trees can be implemented by means of abstract machine instructions. Experiments with benchmarks for forward matching show that our implementation is competitive with tightly coded current state-of-the-art implementations of the other main techniques. In particular, space consumption of context trees is significantly less than for other index structures.     

Color indexing for efficient image retrieval

Content based image retrieval is an active area of research. Many approaches have been proposed to retrieve images based on matching of some features derived from the image content. Color is an important feature of image content. The problem with many traditional matching-based retrieval methods is that the search time for retrieving similar images for a given query image increases linearly with the size of the image database. We present an efficient color indexing scheme for similarity-based retrieval which has a search time that increases logarithmically with the database size.In our approach, the color features are extracted automatically using a color clustering algorithm. Then the cluster centroids are used as representatives of the images in 3-dimensional color space and are indexed using a spatial indexing method that usesR-tree. The worst case search time complexity of this approach isOn _q log(N^* n_avg)), whereN is the number of images in the database, andn _q andn _avg are the number of colors in the query image and the average number of colors per image in the database respectively. We present the experimental results for the proposed approach on two databases consisting of 337 Trademark images and 200 Flag images.     

一种优化的基于LPI的文档索引算法FLPI

LPI对于局部流形结构是优化的, 但在时空上运行效率较低,使其很难应用于大型数据集。基于LPI算法,提出了一种优化的LPI算法FLPI,它将LPI问题分解为一个图嵌入问题和一个正则最小二乘问题,避免了稠密矩阵的特征值分解,显著减少了计算复杂度。此外,在监督环境下,利用一个特别设计的图,使FLPI只需要解决正则最小二乘问题,进一步减少了时空开销。实时数据集实验结果显示,FLPI获得了相似或优于LPI的结果,且运行速度明显提升。     

Conceptual indexing and active retrieval of video for interactive learning environments

Selecting an instructive story from a video case base is an information retrieval problem, but standard indexing and retrieval techniques [1] were not developed with such applications in mind. The classical model assumes a passive retrieval system queried by interested and well-informed users. In educational situations, students cannot be expected to form appropriate queries or to identify their own ignorance. Systems that teach must, therefore, be active retrievers that formulate their own retrieval cues and reason about the appropriateness of intervention.

The Story Producer for InteractivE Learning (SPIEL) is an active retrieval system for recalling stories to tell to students who are learning social skills in a simulated environment [2, 3]. SPIEL is a component of the Guided Social Simulation (GuSS) architecture [4] used to build YELLO, a program that teaches account executives the fine points of selling Yellow Pages advertising. SPIEL uses structured, conceptual indices derived from research in case-based reasoning [5, 6]. SPIEL's manually-created indices are detailed representations of what stories are about, and they are needed to make precise assessments of stories' relevance.

SPIEL's opportunistic retrieval architecture operates in two phases. During the storage phase, the system uses its educational knowledge encapsulated in a library of “storytelling strategies” to determine, for each story, what an opportunity to tell that story would look like. During the retrieval phase, the system tries to recognize those opportunities while the student interacts with the simulation. This design is similar to “opportunistic memory” architectures proposed for opportunistic planning [7, 8].     

Multimedia indexing and retrieval: ever great challenges

In this introduction, we present a brief state of the art of multimedia indexing and retrieval as well as highlight some notions explored in the special issue. We hope that the contributions of this special issue will present ingredients for further investigations on this ever challenging domain. The special issue is actually situated between old problems and new challenges, and contribute to understand the next multimedia indexing and retrieval generation. The contributions explore wide range of fields such as signal processing, data mining and information retrieval.     

Using object and trajectory analysis to facilitate indexing and retrieval of video

This paper aims to show that by using low level feature extraction, motion and object identifying and tracking methods, features can be extracted and indexed for efficient and effective retrieval for video; such as an awards ceremony video. Video scene/shot analysis and key frame extraction are used as a foundation to identify objects in video and be able to find spatial relationships within the video. The compounding of low level features such as colour, texture and abstract object identification lead into higher level real object identification and tracking and scene detection. The main focus is on using a video style that is different to the heavily used sports and news genres. Using different video styles can open the door to creating methods that could encompass all video types instead of specialized methods for each specific style of video.     

A new indexing method based on word proximity for Chinese text retrieval

This paper propsed a novel text representation and matching scheme for Chinese text retrieval.At present,the indexing methods of chinese retrieval systems are either character-based or word-based.The character-based indexing methods,such as bi-gram or tri-gram indexing,have high false drops due to the mismatches between queries and documents.On the other hand,it‘s difficult to efficiently identify all the proper nouns,terminology of different domains,and phrases in the word-based indexing systems.The new indexing method uses both proximity and mutual information of the word paris to represent the text content so as to overcome the high false drop,new word and phrase problems that exist in the character-based and word-based systems.The evaluation results indicate that the average query precision of proximity-based indexing is 5.2% higher than the best results of TREC-5.     

Controlled terminology for clinically-relevant indexing and selective retrieval of biomedical images

Received: 5 April 1997 / Revised: 10 July 1997 / Accepted: 5 August 1997     

Image indexing and retrieval based on vector quantization

To effectively utilize information stored in a digital image library, effective image indexing and retrieval techniques are essential. This paper proposes an image indexing and retrieval technique based on the compressed image data using vector quantization (VQ). By harnessing the characteristics of VQ, the proposed technique is able to capture the spatial relationships of pixels when indexing the image. Experimental results illustrate the robustness of the proposed technique and also show that its retrieval performance is higher compared with existing color-based techniques.     

在线学习视频索引与检索方法

针对交互式的多媒体学习系统的特点,提出了一种基于自然语言的方法来实现基于内容的视频检索,用户可以用自然语言和系统进行交互,从而方便快捷地找到自己想要的视频片段.该方法集成了自然语言处理、实体名提取,基于帧的索引以及信息检索等技术,从而使系统能够处理用户提出的自然语言问题,根据问题构建简洁明了的问题模板,用问题模板与系统中已建的描述视频的模板进行匹配,从而降低了视频检索问题的复杂度,提高了系统的易用性.     

Structural feature indexing for retrieval of partially visible shapes

Efficient and robust information retrieval from large image databases is an essential functionality for the reuse, manipulation, and editing of multimedia documents. Structural feature indexing is a potential approach to efficient shape retrieval from large image databases, but the indexing is sensitive to noise, scales of observation, and local shape deformations. It has now been confirmed that efficiency of classification and robustness against noise and local shape transformations can be improved by the feature indexing approach incorporating shape feature generation techniques (Nishida, Comput. Vision Image Understanding 73 (1) (1999) 121-136). In this paper, based on this approach, an efficient, robust method is presented for retrieval of model shapes that have parts similar to the query shape presented to the image database. The effectiveness is confirmed by experimental trials with a large database of boundary contours obtained from real images, and is validated by systematically designed experiments with a large number of synthetic data.     

A new full-text indexing model with low space overhead for chinese text retrieval

Text retrieval systems require an index to allow efficient retrieval of documents at the cost of some storage overhead. This paper proposes a novel full-text indexing model for Chinese text retrieval based on the concept of adjacency matrix of directed graph. Using this indexing model, on one hand, retrieval systems need to keep only the indexing data, instead of the indexing data and the original text data as the traditional retrieval systems always do. On the other hand, occurrences of index term are identified by labels of the so-called s-strings where the index term appears, rather than by its positions as in traditional indexing models. Consequently, system space cost as a whole can be reduced drastically while retrieval efficiency is maintained satisfactory. Experiments over several real-world Chinese text collections are carried out to demonstrate the effectiveness and efficiency of this model. In addition to Chinese, The proposed indexing model is also effective and efficient for text retrieval of other Oriental languages, such as Japanese and Korean. It is especially useful for digital library application areas where storage resource is very limited (e.g., e-books and CD-based text retrieval systems).     

Using latent semantic indexing for multilanguage information retrieval

In this paper, a method for indexing cross-language databases for conceptual query matching is presented. Two languages (Greek and English) are combined by appending a small portion of documents from one language to the identical documents in the other language. The proposed merging strategy duplicates less than 7% of the entire database (made up of different translations of the Gospels). Previous strategies duplicated up to 34% of the initial database in order to perform the merger. The proposed method retrieves a larger number of relevant documents for both languages with higher cosine rankings when Latent Semantic Indexing (LSI) is employed. Using the proposed merge strategies, LSI is shown to be effective in retrieving documents from either language (Greek or English) without requiring any translation of a user's query. An effective Bible search product needs to allow the use of natural language for searching (queries). LSI enables the user to form queries with using natural expressions in the user's own native language. The merging strategy proposed in this study enables LSI to retrieve relevant documents effectively using a minimum of the database in a foreign language.Michael W. Berry is an Assistant Professor in the Department of Computer Science at the University of Tennessee, Knoxville. He recieved a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1990, and an M.S. in Applied Mathematics from North Carolina State University at Raleigh in 1983. His current interests include scientific computing, parallel algorithms, information retrieval applications, and computer performance evaluation. He is a member of the ACM, SIAM, and the IEEE Computer Society.Paul G. Young is now employed as an Associate Consultant with Oracle Government Services in Knoxville, TN. In 1984 he graduated from the Gordon-Conwell Theological Seminary in S. Hamilton, MA and became an Ordained Presbyterian Minister (PCUSA). He later received an M.S. in Computer Science from the University of Tennessee in 1994.     

Query-driven indexing for scalable peer-to-peer text retrieval

In this paper, we present a query-driven indexing/retrieval strategy for efficient full text retrieval from large document collections distributed within a structured P2P network. Our indexing strategy is based on two important properties: (1) the generated distributed index stores posting lists for carefully chosen indexing term combinations that are frequently present in user queries, and (2) the posting lists containing too many document references are truncated to a bounded number of their top-ranked elements. These two properties guarantee acceptable latency and bandwidth requirements, essentially because the number of indexing term combinations remains scalable and the posting lists transmitted during retrieval never exceed a constant size. A novel index update mechanism efficiently handles adding of new documents to the document collection. Thus, the generated distributed index corresponds to a constantly evolving query-driven indexing structure that efficiently follows current information needs of the users and changes in the document collection.We show that the size of the index and the generated indexing/retrieval traffic remains manageable even for Web-size document collections at the price of a marginal loss in precision for rare queries. Our theoretical analysis and experimental results provide convincing evidence about the feasibility of the query-driven indexing strategy for large scale P2P text retrieval.     

Wavelet correlogram: A new approach for image indexing and retrieval

In this paper, a new algorithm for content-based image indexing and retrieval is presented. The proposed method is based on a combination of multiresolution image decomposition and color correlation histogram. According to the new algorithm, wavelet coefficients of the image are computed first using a directional wavelet transform such as Gabor wavelets. A quantization step is then applied before computing one-directional autocorrelograms of the wavelet coefficients. Finally, index vectors are constructed using these one-directional wavelet correlograms. The retrieval results obtained by application of our new method on a 1000 image database demonstrated a significant improvement in effectiveness and efficiency compared to the indexing and retrieval methods based on image color correlogram or wavelet transform.     

Web-based image indexing and retrieval in JPEG compressed domain

As the majority of content-based image retrieval systems operate on full images in pixel domain, decompression is a prerequisite for the retrieval of compressed images. To provide a possible on-line indexing and retrieval technique for those jpg image files, we propose a novel pseudo-pixel extraction algorithm to bridge the gap between the existing image indexing technology, developed in the pixel domain, and the fact that an increasing number of images stored on the Web are already compressed by JPEG at the source. Further, we describe our Web-based image retrieval system, WEBimager, by using the proposed algorithm to provide a prototype visual information system toward automatic management, indexing, and retrieval of compressed images available on the Internet. This provides users with efficient tools to search the Web for compressed images and establish a database or a collection of special images to their interests. Experiments using texture- and colour-based indexing techniques support the idea that the proposed algorithm achieves significantly better results in terms of computing cost than their full decompression or partial decompression counterparts. This technology will help control the explosion of media-rich content by offering users a powerful automated image indexing and retrieval tool for compressed images on the Web.J. Jiang: Contacting author     

A modular and adaptive framework for large scale video indexing and content‐based retrieval: the SIRSALE system

In this paper, we present the design and the implementation of SIRSALE: a distributed video data management system. SIRSALE allows users to manipulate video streams stored in large distributed repositories, i.e. it provides remote users with functionalities to browse video streams by structures (shots, scenes, sequences, etc.), to annotate the semantic contents of videos and to query the distributed video repositories. One of the main contributions of SIRSALE is its contextual adaptation to the target application, i.e. it is based on a modular data model that allows adapting the system to deal with several semantic contexts. In other words, SIRSALE allows users to define and to use their own semantic data model in order to annotate and query video databases. The key idea behind this is to dynamically adapt the whole system, mainly user interfaces, to stand several semantic data models. The system has been presented to professionals who gave a positive feedback. Copyright © 2006 John Wiley & Sons, Ltd.     

DocMIR: An automatic document-based indexing system for meeting retrieval

This paper describes the DocMIR system which captures, analyzes and indexes automatically meetings, conferences, lectures, etc. by taking advantage of the documents projected (e.g. slideshows, budget tables, figures, etc.) during the events. For instance, the system can automatically apply the above-mentioned procedures to a lecture and automatically index the event according to the presented slides and their contents. For indexing, the system requires neither specific software installed on the presenter’s computer nor any conscious intervention of the speaker throughout the presentation. The only material required by the system is the electronic presentation file of the speaker. Even if not provided, the system would temporally segment the presentation and offer a simple storyboard-like browsing interface. The system runs on several capture boxes connected to cameras and microphones that records events, synchronously. Once the recording is over, indexing is automatically performed by analyzing the content of the captured video containing projected documents and detects the scene changes, identifies the documents, computes their duration and extracts their textual content. Each of the captured images is identified from a repository containing all original electronic documents, captured audio–visual data and metadata created during post-production. The identification is based on documents’ signatures, which hierarchically structure features from both layout structure and color distributions of the document images. Video segments are finally enriched with textual content of the identified original documents, which further facilitate the query and retrieval without using OCR. The signature-based indexing method proposed in this article is robust and works with low-resolution images and can be applied to several other applications including real-time document recognition, multimedia IR and augmented reality systems.