Representing and Reasoning on Conceptual Queries Over Image Databases

The problem of content management of multimedia data types (e.g., image, video, graphics) is becoming increasingly important with the development of advanced multimedia applications. Traditional database management systems are inadequate for the handling of such data types. They require new techniques for query formulation, retrieval, evaluation, and navigation. In this paper we develop a knowledge-based framework for modeling and retrieving image data by content. To represent the various aspects of an image object's characteristics, we propose a model which consists of three layers: (1) Feature and Content Layer, intended to contain image visual features such as contours, shapes,etc.; (2) Object Layer, which provides the (conceptual) content dimension of images; and (3) Schema Layer, which contains the structured abstractions of images, i.e., a general schema about the classes of objects represented in the object layer. We propose two abstract languages on the basis of description logics: one for describing knowledge of the object and schema layers, and the other, more expressive, for making queries. Queries can refer to the form dimension (i.e., information of the Feature and Content Layer) or to the content dimension (i.e., information of the Object Layer). These languages employ a variable free notation, and they are well suited for the design, verification and complexity analysis of algorithms. As the amount of information contained in the previous layers may be huge and operations performed at the Feature and Content Layer are time-consuming, resorting to the use of materialized views to process and optimize queries may be extremely useful. For that, we propose a formal framework for testing containment of a query in a view expressed in our query language. The algorithm we propose is sound and complete and relatively efficient.This is an extended version of the article in: Eleventh International Symposium on Methodologies for Intelligent Systems, Warsaw, Poland, 1999

Graph-based Interactive Bibliographic Information Retrieval Systems

In the big data era, we have witnessed the explosion of scholarly literature. This explosion has imposed challenges to the retrieval of bibliographic information. Retrieval of intended bibliographic information has become challenging due to the overwhelming search results returned by bibliographic information retrieval systems for given input queries. At the same time, users’ bibliographic information needs have become more specific such that only information that best matches their needs is seen as relevant. Current bibliographic information retrieval systems such as Web of Science, Scopus, and Google Scholar have become an unalienable component in searching bibliographic data. However, these systems have limited support of complex bibliographic queries. For example, a query- “papers on information retrieval, which were cited by John’s papers that had been presented in SIGIR” is an ordinary information need that researchers may have, but is not appropriately representable in these systems. In addition, these systems only support search for papers and do not support other bibliographic entities such as authors and terms as the final search results. Therefore, in this dissertation, we propose several bibliographic information retrieval systems that can address complex bibliographic queries. We propose form-, natural language-, and visual graph-based systems that allow users to formulate bibliographic queries in a variety of ways. The form-based system allows users to formulate queries by selecting forms and input values in those selected forms. In the natural language-based system, users formulate queries using a natural language. Users formulate queries by drawing nodes and links in the visual graph-based system. These systems are based on a graph model to enhance retrieval efficiency and provides interfaces for users to formulate queries interactively. Through a system-centered evaluation, we find that our graph-based system took less time to process complex queries than a relational-entity-based system (two secs vs. several mins on average). In addition, our visual graph-based system can deal with the representation of advanced queries such as bibliographic coupling, paper co-citation, and author co-citation, while current bibliographic information systems do not support these queries. A user-centered evaluation reveals that participants rated the natural language-based system the most useful, easy to use, and easy to learn. Participants also reported that the form-based system was easier to learn than the visual graph-based system. Based on the results of a usability evaluation, we find that the form-based system is preferred for low-complexity tasks while the visual graph-based system is preferred for high-complexity tasks. The strength of the natural language-based system is that no additional effort is needed to formulate more complex queries. The proposed systems are effective and efficient solutions for addressing complex bibliographic information needs. In addition, we believe the experimental design and results shown in this paper can serve as a useful guideline and benchmark for future studies.Ph.D., Information Studies -- Drexel University, 201

NITELIGHT: A Graphical Tool for Semantic Query Construction

Query formulation is a key aspect of information retrieval, contributing to both the efficiency and usability of many semantic applications. A number of query languages, such as SPARQL, have been developed for the Semantic Web; however, there are, as yet, few tools to support end users with respect to the creation and editing of semantic queries. In this paper we introduce a graphical tool for semantic query construction (NITELIGHT) that is based on the SPARQL query language specification. The tool supports end users by providing a set of graphical notations that represent semantic query language constructs. This language provides a visual query language counterpart to SPARQL that we call vSPARQL. NITELIGHT also provides an interactive graphical editing environment that combines ontology navigation capabilities with graphical query visualization techniques. This paper describes the functionality and user interaction features of the NITELIGHT tool based on our work to date. We also present details of the vSPARQL constructs used to support the graphical representation of SPARQL queries

An Investigation on Text-Based Cross-Language Picture Retrieval Effectiveness through the Analysis of User Queries

Purpose: This paper describes a study of the queries generated from a user experiment for cross-language information retrieval (CLIR) from a historic image archive. Italian speaking users generated 618 queries for a set of known-item search tasks. The queries generated by user’s interaction with the system have been analysed and the results used to suggest recommendations for the future development of cross-language retrieval systems for digital image libraries. Methodology: A controlled lab-based user study was carried out using a prototype Italian-English image retrieval system. Participants were asked to carry out searches for 16 images provided to them, a known-item search task. User’s interactions with the system were recorded and queries were analysed manually quantitatively and qualitatively. Findings: Results highlight the diversity in requests for similar visual content and the weaknesses of Machine Translation for query translation. Through the manual translation of queries we show the benefits of using high-quality translation resources. The results show the individual characteristics of user’s whilst performing known-item searches and the overlap obtained between query terms and structured image captions, highlighting the use of user’s search terms for objects within the foreground of an image. Limitations and Implications: This research looks in-depth into one case of interaction and one image repository. Despite this limitation, the discussed results are likely to be valid across other languages and image repository. Value: The growing quantity of digital visual material in digital libraries offers the potential to apply techniques from CLIR to provide cross-language information access services. However, to develop effective systems requires studying user’s search behaviours, particularly in digital image libraries. The value of this paper is in the provision of empirical evidence to support recommendations for effective cross-language image retrieval system design.</p

Overview of the 2005 cross-language image retrieval track (ImageCLEF)

The purpose of this paper is to outline efforts from the 2005 CLEF crosslanguage image retrieval campaign (ImageCLEF). The aim of this CLEF track is to explore the use of both text and content-based retrieval methods for cross-language image retrieval. Four tasks were offered in the ImageCLEF track: a ad-hoc retrieval from an historic photographic collection, ad-hoc retrieval from a medical collection, an automatic image annotation task, and a user-centered (interactive) evaluation task that is explained in the iCLEF summary. 24 research groups from a variety of backgrounds and nationalities (14 countries) participated in ImageCLEF. In this paper we describe the ImageCLEF tasks, submissions from participating groups and summarise the main fndings

Multi modal multi-semantic image retrieval

PhDThe rapid growth in the volume of visual information, e.g. image, and video can overwhelm users’ ability to find and access the specific visual information of interest to them. In recent years, ontology knowledge-based (KB) image information retrieval techniques have been adopted into in order to attempt to extract knowledge from these images, enhancing the retrieval performance. A KB framework is presented to promote semi-automatic annotation and semantic image retrieval using multimodal cues (visual features and text captions). In addition, a hierarchical structure for the KB allows metadata to be shared that supports multi-semantics (polysemy) for concepts. The framework builds up an effective knowledge base pertaining to a domain specific image collection, e.g. sports, and is able to disambiguate and assign high level semantics to ‘unannotated’ images. Local feature analysis of visual content, namely using Scale Invariant Feature Transform (SIFT) descriptors, have been deployed in the ‘Bag of Visual Words’ model (BVW) as an effective method to represent visual content information and to enhance its classification and retrieval. Local features are more useful than global features, e.g. colour, shape or texture, as they are invariant to image scale, orientation and camera angle. An innovative approach is proposed for the representation, annotation and retrieval of visual content using a hybrid technique based upon the use of an unstructured visual word and upon a (structured) hierarchical ontology KB model. The structural model facilitates the disambiguation of unstructured visual words and a more effective classification of visual content, compared to a vector space model, through exploiting local conceptual structures and their relationships. The key contributions of this framework in using local features for image representation include: first, a method to generate visual words using the semantic local adaptive clustering (SLAC) algorithm which takes term weight and spatial locations of keypoints into account. Consequently, the semantic information is preserved. Second a technique is used to detect the domain specific ‘non-informative visual words’ which are ineffective at representing the content of visual data and degrade its categorisation ability. Third, a method to combine an ontology model with xi a visual word model to resolve synonym (visual heterogeneity) and polysemy problems, is proposed. The experimental results show that this approach can discover semantically meaningful visual content descriptions and recognise specific events, e.g., sports events, depicted in images efficiently. Since discovering the semantics of an image is an extremely challenging problem, one promising approach to enhance visual content interpretation is to use any associated textual information that accompanies an image, as a cue to predict the meaning of an image, by transforming this textual information into a structured annotation for an image e.g. using XML, RDF, OWL or MPEG-7. Although, text and image are distinct types of information representation and modality, there are some strong, invariant, implicit, connections between images and any accompanying text information. Semantic analysis of image captions can be used by image retrieval systems to retrieve selected images more precisely. To do this, a Natural Language Processing (NLP) is exploited firstly in order to extract concepts from image captions. Next, an ontology-based knowledge model is deployed in order to resolve natural language ambiguities. To deal with the accompanying text information, two methods to extract knowledge from textual information have been proposed. First, metadata can be extracted automatically from text captions and restructured with respect to a semantic model. Second, the use of LSI in relation to a domain-specific ontology-based knowledge model enables the combined framework to tolerate ambiguities and variations (incompleteness) of metadata. The use of the ontology-based knowledge model allows the system to find indirectly relevant concepts in image captions and thus leverage these to represent the semantics of images at a higher level. Experimental results show that the proposed framework significantly enhances image retrieval and leads to narrowing of the semantic gap between lower level machinederived and higher level human-understandable conceptualisation

Identification of Design Principles

This report identifies those design principles for a (possibly new) query and transformation language for the Web supporting inference that are considered essential. Based upon these design principles an initial strawman is selected. Scenarios for querying the Semantic Web illustrate the design principles and their reflection in the initial strawman, i.e., a first draft of the query language to be designed and implemented by the REWERSE working group I4

Overview of the ImageCLEFphoto 2008 photographic retrieval task

ImageCLEFphoto 2008 is an ad-hoc photo retrieval task and part of the ImageCLEF evaluation campaign. This task provides both the resources and the framework necessary to perform comparative laboratory-style evaluation of visual information retrieval systems. In 2008, the evaluation task concentrated on promoting diversity within the top 20 results from a multilingual image collection. This new challenge attracted a record number of submissions: a total of 24 participating groups submitting 1,042 system runs. Some of the findings include that the choice of annotation language is almost negligible and the best runs are by combining concept and content-based retrieval methods

Multimedia information technology and the annotation of video

The state of the art in multimedia information technology has not progressed to the point where a single solution is available to meet all reasonable needs of documentalists and users of video archives. In general, we do not have an optimistic view of the usability of new technology in this domain, but digitization and digital power can be expected to cause a small revolution in the area of video archiving. The volume of data leads to two views of the future: on the pessimistic side, overload of data will cause lack of annotation capacity, and on the optimistic side, there will be enough data from which to learn selected concepts that can be deployed to support automatic annotation. At the threshold of this interesting era, we make an attempt to describe the state of the art in technology. We sample the progress in text, sound, and image processing, as well as in machine learning