Search (49 results, page 1 of 3)

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Abstract

This paper centres on the tools for the management of new digital documents, which are not only textual, but also visual-video, audio or multimedia in the full sense. Among the aims is to demonstrate that operating within the terms of generic Information Retrieval through textual language only is limiting, and it is instead necessary to consider ampler criteria, such as those of MultiMedia Information Retrieval, according to which, every type of digital document can be analyzed and searched by the proper elements of language for its proper nature. MMIR is presented as the organic complex of the systems of Text Retrieval, Visual Retrieval, Video Retrieval, and Audio Retrieval, each of which has an approach to information management that handles the concrete textual, visual, audio, or video content of the documents directly, here defined as content-based. In conclusion, the limits of this content-based objective access to documents is underlined. The discrepancy known as the semantic gap is that which occurs between semantic-interpretive access and content-based access. Finally, the integration of these conceptions is explained, gathering and composing the merits and the advantages of each of the approaches and of the systems to access to information.

Date

22. 1.2012 13:02:10

Footnote

Bezugnahme auf: Enser, P.G.B.: Visual image retrieval. In: Annual review of information science and technology. 42(2008), S.3-42.

Source

Knowledge organization. 39(2012) no.1, S.13-22

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Abstract

Purpose - To provide a better-informed view of the extent of the semantic gap in image retrieval, and the limited potential for bridging it offered by current semantic image retrieval techniques. Design/methodology/approach - Within an ongoing project, a broad spectrum of operational image retrieval activity has been surveyed, and, from a number of collaborating institutions, a test collection assembled which comprises user requests, the images selected in response to those requests, and their associated metadata. This has provided the evidence base upon which to make informed observations on the efficacy of cutting-edge automatic annotation techniques which seek to integrate the text-based and content-based image retrieval paradigms. Findings - Evidence from the real-world practice of image retrieval highlights the existence of a generic-specific continuum of object identification, and the incidence of temporal, spatial, significance and abstract concept facets, manifest in textual indexing and real-query scenarios but often having no directly visible presence in an image. These factors combine to limit the functionality of current semantic image retrieval techniques, which interpret only visible features at the generic extremity of the generic-specific continuum. Research limitations/implications - The project is concerned with the traditional image retrieval environment in which retrieval transactions are conducted on still images which form part of managed collections. The possibilities offered by ontological support for adding functionality to automatic annotation techniques are considered. Originality/value - The paper offers fresh insights into the challenge of migrating content-based image retrieval from the laboratory to the operational environment, informed by newly-assembled, comprehensive, live data.

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Abstract

In this paper, we focus on applying sentiment analysis to resources from online art collections, by exploiting, as information source, tags intended as textual traces that visitors leave to comment artworks on social platforms. We present a framework where methods and tools from a set of disciplines, ranging from Semantic and Social Web to Natural Language Processing, provide us the building blocks for creating a semantic social space to organize artworks according to an ontology of emotions. The ontology is inspired by the Plutchik's circumplex model, a well-founded psychological model of human emotions. Users can be involved in the creation of the emotional space, through a graphical interactive interface. The development of such semantic space enables new ways of accessing and exploring art collections. The affective categorization model and the emotion detection output are encoded into W3C ontology languages. This gives us the twofold advantage to enable tractable reasoning on detected emotions and related artworks, and to foster the interoperability and integration of tools developed in the Semantic Web and Linked Data community. The proposal has been evaluated against a real-word case study, a dataset of tagged multimedia artworks from the ArsMeteo Italian online collection, and validated through a user study.

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Abstract

Although images are visual information sources with little or no text associated with them, users still tend to use text to describe images and formulate queries. This is because digital libraries and search engines provide mostly text query options and rely on text annotations for representation and retrieval of the semantic content of images. While the main focus of image research is on indexing and retrieval of individual images, the general topic of image browsing and indexing, and retrieval of groups of images has not been adequately investigated. Comparisons of descriptions of individual images as well as labels of groups of images supplied by users using cognitive models are scarce. This work fills this gap. Using the basic level theory as a framework, a comparison of the descriptions of individual images and labels assigned to groups of images by 180 participants in three studies found a marked difference in their level of abstraction. Results confirm assertions by previous researchers in LIS and other fields that groups of images are labeled using more superordinate level terms while individual image descriptions are mainly at the basic level. Implications for design of image browsing interfaces, taxonomies, thesauri, and similar tools are discussed.

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Abstract

Emotional meaning is critical for users to retrieve relevant images. However, because emotional meanings are subject to the individual viewer's interpretation, they are considered difficult to implement when designing image retrieval systems. With the intent of making an image's emotional messages more readily accessible, this study aims to test a new approach designed to enhance the accessibility of emotional meanings during the image search process. This approach utilizes image searchers' emotional reactions, which are quantitatively measured. Broadly used quantitative measurements for emotional reactions, Semantic Differential (SD) and Self-Assessment Manikin (SAM), were selected as tools for gathering users' reactions. Emotional representations obtained from these two tools were compared with three image perception tasks: searching, describing, and sorting. A survey questionnaire with a set of 12 images was administered to 58 participants, which were tagged with basic emotions. Results demonstrated that the SAM represents basic emotions on 2-dimensional plots (pleasure and arousal dimensions), and this representation consistently corresponded to the three image perception tasks. This study provided experimental evidence that quantitative users' reactions can be a useful complementary element of current image retrieval/indexing systems. Integrating users' reactions obtained from the SAM into image browsing systems would reduce the efforts of human indexers as well as improve the effectiveness of image retrieval systems.

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Abstract

Some collections cover many topics, while others are narrowly focused an a limited number of topics. We introduce the concept of the "scope" of a collection of documents and we compare two ways of measuring lt. These measures are based an the distances between documents. The first uses the overlap of words between pairs of documents. The second measure uses a novel method that calculates the semantic relatedness to pairs of words from the documents. Those values are combined to obtain an overall distance between the documents. The main validation for the measures compared Web pages categorized by Yahoo. Sets of pages sampied from broad categories were determined to have a higher scope than sets derived from subcategories. The measure was significant and confirmed the expected difference in scope. Finally, we discuss other measures related to scope.

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Date

5. 8.2006 13:22:08

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Abstract

The paper establishes a taxonomy of image-text relationships that reflects the ways that images and text interact. It is applicable to all subject areas and document types. The taxonomy was developed to answer the research question: how does an illustration relate to the text with which it is associated, or, what are the functions of illustration? Developed in a two-stage process - first, analysis of relevant research in children's literature, dictionary development, education, journalism, and library and information design and, second, subsequent application of the first version of the taxonomy to 954 image-text pairs in 45 Web pages (pages with educational content for children, online newspapers, and retail business pages) - the taxonomy identifies 49 relationships and groups them in three categories according to the closeness of the conceptual relationship between image and text. The paper uses qualitative content analysis to illustrate use of the taxonomy to analyze four image-text pairs in government publications and discusses the implications of the research for information retrieval and document design.

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Abstract

Multimedia data can require significant examination time to find desired features ('content analysis'). An alternative is using natural-language captions to describe the data, and matching captions to English queries. But it is hard to include everything in the caption of a complicated datum, so significant content analysis may still seem required. We discuss linguistic clues in captions, both syntactic and semantic, that can simplify or eliminate content analysis. We introduce the notion of content depiction and ruled for depiction inference. Our approach is implemented in an expert system which demonstrated significant increases in recall in experiments

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Abstract

We describe here NKRL (Narrative Knowledge Representation Language), a semantic formalism for taking into account the characteristics of narrative multimedia documents. In these documents, the information content consists in the description of 'events' that relate the real or intended behaviour of some 'actors' (characters, personages, etc.). Narrative documents of an economic interest correspond to news stories, corporate documents, normative and legal texts, intelligence messages, representation of patient's medical records, etc. NKRL is characterised by the use of several knowledge representation principles and several high-level inference tools.

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Abstract

Examines Panofsky's and Barthes's theories of image analysis and reports on a study of criteria for analysis and indexing of images and the different types of user queries used in 15 Danish newspaper image archives. A structured interview method and observation and various categories for subject analysis were used. The results identify a list of the minimum number of elements and led to user typology of 5 categories. The requirement for retrieval may involve combining images in a more visual way with text-based image retrieval

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Abstract

Examines how an analytical framework to describe the contents of images may be extended to deal with time based materials like film and music. A levels of meanings table was developed and used as an indexing template for image retrieval purposes. Develops a concept of democratic indexing which focused on user interpretation. Describes the approach to image or pictorial information retrieval. Extends the approach in relation to fiction

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Abstract

The arts, humanities, and social sciences commonly borrow concepts and methods from the sciences, but interdisciplinary borrowing seldom occurs in the opposite direction. Research on narrative discourse is relevant to problems of documentary storage and retrieval, for the arts and humanities in particular, but also for other broad areas of knowledge. This paper views the potential application of narrative discourse analysis to information storage and retrieval problems from 2 perspectives: 1) analysis and comparison of narrative documents in all disciplines may be simplified if fundamental categories that occur in narrative documents can be isolated; and 2) the possibility of subdividing the world of knowledge initially into narrative and non-narrative documents is explored with particular attention to Werlich's work on text types

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Abstract

This paper argues that the realisation of the electronic hypermedia of the future depends on integrating the technology of free text retrieval with the classification-based discipline of content analysis

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Abstract

Visual materials represent a significantly different type of communication to textual materials and therefore present distinct challenges for the process of retrieval, especially if by retireval we mean intellectual access to the content of images. This paper outlines the special characteristics of visual materials, focusing on their pontential complexity and subjectivity, and the methods used and explored for gaining access to visual materials as reported in the literature. It concludes that methods of access to visual materials are dominated by the relative mature systems developed for textual materials and that access methods based on visual communication are still largely in the developmental or prototype stage. Although reported research on user requirements in the retrieval of visual information is noticeably lacking, the results of at least one study indicate that the visually-based retrieval methods of structured and unstructered browsing seem to be preferred for visula materials and that effective retrieval methods are ultimately related to characteristics of the enquirer and the visual information sought

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Abstract

Information search and retrieval interactions usually involve information content in the form of document collections, information retrieval systems and interfaces, and the user. To fully understand information search and retrieval interactions between users' cognitive space and the information space, researchers need to turn to cognitive models and theories. In this article, the authors use one of these theories, the basic level theory. Use of the basic level theory to understand human categorization is both appropriate and essential to user-centered design of taxonomies, ontologies, browsing interfaces, and other indexing tools and systems. Analyses of data from two studies involving free sorting by 105 participants of 100 images were conducted. The types of categories formed and category labels were examined. Results of the analyses indicate that image category labels generally belong to superordinate to the basic level, and are generic and interpretive. Implications for research on theories of cognition and categorization, and design of image indexing, retrieval and browsing systems are discussed.