Visual Information Retrieval

Content-Based Image Retrieval (CBIR) systems are required to effectively harness infor- mation from ubiquitous image collections. Despite intense research efforts by the multidis- ciplinary CBIR community since early 1990s, apparently there is a mismatch between these advances and the one that is truly required to bring success to CBIR in the commercial mar- ket place. In this paper we provide

A visual query is based on pictorial representation of conceptual entities and operations. One of the most important features used in visual queries is the shape. Despite its intuitive writing, a shape-based visual query usually suffers of a complexity processing related to two major parameters: 1-the imprecise user request due to the user certainty or to the vagueness of his need, 2-shapes may undergo several types of transformation like occlusion, rotation, etc. that need to be considered. This is why several methods are provided in the literature to assist the user during query writing. On one hand, relevance feedback technique is widely used to rewrite the initial user query. On the other hand, shape transformations are considered by current shape-based retrieval approaches without any user intervention. In this paper, we present a new cooperative approach based on the shape neighbourhood concept allowing the user to rewrite a shape-based visual query according to his preference...

Extensible Indexing is a SQL-based framework that allows users to define domain-specific indexing schemes, and integrate them into the Oracle8i server. Users register a new indexing scheme, the set of related operators, and additional properties through SQL data ...

ion --- VIR systems differ in the level of abstraction in which content is indexed. For example, images may be indexed at various levels, such as at the feature-level (e.g., color, texture, and shape), object-level (e.g., moving foreground object), syntax-level (e.g., video shot), and semantic-level (e.g., image subject), and so forth. Most automatic VIR systems aim at lowlevel features, while the high-level indexes are usually generated manually. Interaction among different levels is an exciting but unsolved issue. . Generality --- VIR systems differ in their specificity of the domain of visual information. For example, customized feature sets can be developed to incorporate specific domain knowledge, such as those in medical and remote-sensing applications. Other, more general VIR systems aim at indexing unconstrained visual information such as that on the Internet. . Content Collection --- VIR systems differ in the methods in which new visual information is ad...

This paper reviews the technical aspects of the state-of-art techniques and procedures for image acquisition, pre-processing, image processing and analysis, search and retrieval techniques for leukaemia blood cell images. Hospital Ampang gets a fair amount of leukaemia cases every year but the hematology department of Hospital Ampang lacks the digital format of patient management system and leukaemia images storage and retrieval system. So, a survey on technical aspects of acquisition, analysis and retrieval was conducted to propose and develop patient management system and leukaemia diagnosis support system for Hospital Ampang, Malaysia. © 2008 Springer-Verlag.

link.springer.com/chapter/10.1007/978-3-540-69139-6_60

This chapter describes several approaches for information fusion that have been used in ImageCLEF over the past seven years. In this context, the fusion of information is mainly meant to combine textual and visual retrieval. Data fusion techniques from 116 papers (62% of ImageCLEF working notes) are categorized, described and discussed. It was observed that three general approaches were used for retrieval that can be categorized based on the system level chosen for combining modalities: 1) at the input of the system with inter–media query expansion, 2) internally to the system with early fusion and 3) at the output of the system with late fusion which is by far the most widely used fusion strategy.

In this article, we introduce an application of swarm intelligence to distributed visual information retrieval distributed over networks. Based on the relevance feedback scheme, we use ant-like agents to crawl the network and to retrieve relevant images. Agents movements are influenced by markers stored on the hosts. These markers are reinforced to match the distribution of relevant images over the network. We tackle the use of the information gathered during previous search sessions. In order to match the different categories available on the network, we use several markers. Sessions searching for the same category will thus use the same makers. The system involves three learning problems: the selection of relevant markers regarding the searched category, the reinforcement of these markers and the learning of the relevance function. All of these problems are based on the relevance feedback loop. We test our system on a custom network hosting images taken from the well known TrecVid dataset. Our system shows a high improvement over classical content based image retrieval systems which do not use previous sessions information.

Search engines are the most powerful resources for finding information on the rapidly expanding World Wide Web (WWW). Finding the desired search engines and learning how to use them, however, can be very time consuming. The integration of such search tools enables the users to access information across the world in a transparent and efficient manner. These systems are called meta-search engines. The recent emergence of visual information retrieval (VIR) search engines on the web is leading to the same efficiency problem. This paper describes and evaluates MetaSEEk, a content-based meta-search engine used for finding images on the Web based on their visual information. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. User feedback is also integrated in the ranking refinement. We compare MetaSEEk with a base line version of meta-search engine, which does not use the...

Medical visual information retrieval has been a very active research area over the past ten years as an increasing amount of images is produced digitally and made available in the electronic patient record. Tools are required to give access to the images and exploit the information inherently stored in medical cases including images. To compare image retrieval techniques of research prototypes based on the same data and tasks, ImageCLEF was started in 2003 and a medical task was added in 2004. Since then, every year a database was distributed, tasks developed, and systems compared based on realistic search tasks and large databases. For the year 2007 a set of almost 68,000 images was distributed among 38 research groups registered for the medical retrieval task. Realistic query topics were developed based on a log file of Medline. This log file contains the queries performed on Pubmed during 24 hours. Most queries could not be used as search topics directly as they do not contain im...

A visual information retrieval environment provides visualization features that help users manage the large result sets that are typical in many information retrieval environments. In many of these visual interfaces the spatial layout of document surrogates is used to communicate ...

Abstract—With the explosion in demand for visual information retrieval in soccer videos, many Content-Based Video Retrieval (CBVR) models were born. However these current CBVR models still remain some shortcomings such as inflexible retrieval frameworks because they are ...

We present a new visualization approach for metadata combining different visualizations into a so-called Su-perTable accompanied by a Scatterplot. The goal is to improve user experience during the information seeking process. Our new visualizations are based on our experi-ences developing a visual information retrieval system called INSYDER to supply small and medium size enter-prises with business information from the Internet. Based on extensive user tests the original visualizations have been redesigned in two different design variants. Instead of offering multiple visualizations to choose from the Su-perTable + Scatterplot combines them in a new way. Therefore, the user has the feeling that he is working with one single visualization in different states. Further the SuperTable solves a problem which seemed to be imma-nent to visualizations in document retrieval: the change of modalities.

Search engines are the most powerful resources for finding information on the rapidly expanding World-Wide Web. Finding the desired search engines and learning how to use them, however, can be very time consuming. Metasearch engines, which integrate a group of such search tools, enable users to access information across the world in a transparent and more efficient manner. The recent emergence of visual information retrieval (VIR) systems on the Web is leading to the same efficiency problem. This paper describes MetaSEEk, a meta-search en gine us d for retrieving images based on their visual content on the Web. MetaSEEk is designed to intelligently select and interface with multiple on-line image search engines by ranking their performance for different classes of user queries. User feedback is also integrated in the ranking refinement. MetaSEEk has been developed to explore the issues involved in querying large, distributed, on-line visual information system sources. We compare Met...