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

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings

Ontology-Based MEDLINE Document Classification

An increasing and overwhelming amount of biomedical information is available in the research literature mainly in the form of free-text. Biologists need tools that automate their information search and deal with the high volume and ambiguity of free-text. Ontologies can help automatic information processing by providing standard concepts and information about the relationships between concepts. The Medical Subject Headings (MeSH) ontology is already available and used by MEDLINE indexers to annotate the conceptual content of biomedical articles. This paper presents a domain-independent method that uses the MeSH ontology inter-concept relationships to extend the existing MeSH-based representation of MEDLINE documents. The extension method is evaluated within a document triage task organized by the Genomics track of the 2005 Text REtrieval Conference (TREC). Our method for extending the representation of documents leads to an improvement of 17% over a non-extended baseline in terms of normalized utility, the metric defined for the task. The SVMlight software is used to classify documents

Using semantic indexing to improve searching performance in web archives

The sheer volume of electronic documents being published on the Web can be overwhelming for users if the searching aspect is not properly addressed. This problem is particularly acute inside archives and repositories containing large collections of web resources or, more precisely, web pages and other web objects. Using the existing search capabilities in web archives, results can be compromised because of the size of data, content heterogeneity and changes in scientific terminologies and meanings. During the course of this research, we will explore whether semantic web technologies, particularly ontology-based annotation and retrieval, could improve precision in search results in multi-disciplinary web archives

A Semantic-Based Information Management System to Support Innovative Product Design

International competition and the rapidly global economy, unified by improved communication and transportation, offer to the consumers an enormous choice of goods and services. The result is that companies now require quality, value, time to market and innovation to be successful in order to win the increasing competition. In the engineering sector this is traduced in need of optimization of the design process and in maximization of re-use of data and knowledge already existing in the company. The “SIMI-Pro” (Semantic Information Management system for Innovative Product design) system addresses specific deficiencies in the conceptual phase of product design when knowledge management, if applied, is often sectorial. Its main contribution is in allowing easy, fast and centralized collection of data from multiple sources and in supporting the retrieval and re-use of a wide range of data that will help stylists and engineers shortening the production cycle. SIMI-Pro will be one of the first prototypes to base its information management and its knowledge sharing system on process ontology and it will demonstrate how the use of centralized network systems, coupled with Semantic Web technologies, can improve inter-working activities and interdisciplinary knowledge sharing

Utilising semantic technologies for intelligent indexing and retrieval of digital images

The proliferation of digital media has led to a huge interest in classifying and indexing media objects for generic search and usage. In particular, we are witnessing colossal growth in digital image repositories that are difficult to navigate using free-text search mechanisms, which often return inaccurate matches as they in principle rely on statistical analysis of query keyword recurrence in the image annotation or surrounding text. In this paper we present a semantically-enabled image annotation and retrieval engine that is designed to satisfy the requirements of the commercial image collections market in terms of both accuracy and efficiency of the retrieval process. Our search engine relies on methodically structured ontologies for image annotation, thus allowing for more intelligent reasoning about the image content and subsequently obtaining a more accurate set of results and a richer set of alternatives matchmaking the original query. We also show how our well-analysed and designed domain ontology contributes to the implicit expansion of user queries as well as the exploitation of lexical databases for explicit semantic-based query expansion

Applying semantic web technologies to knowledge sharing in aerospace engineering

This paper details an integrated methodology to optimise Knowledge reuse and sharing, illustrated with a use case in the aeronautics domain. It uses Ontologies as a central modelling strategy for the Capture of Knowledge from legacy docu-ments via automated means, or directly in systems interfacing with Knowledge workers, via user-defined, web-based forms. The domain ontologies used for Knowledge Capture also guide the retrieval of the Knowledge extracted from the data using a Semantic Search System that provides support for multiple modalities during search. This approach has been applied and evaluated successfully within the aerospace domain, and is currently being extended for use in other domains on an increasingly large scale

Knowledge Search within a Company-WIKI

The usage of Wikis for the purpose of knowledge management within a business company is only of value if the stored information can be found easily. The fundamental characteristic of a Wiki, its easy and informal usage, results in large amounts of steadily changing, unstructured documents. The widely used full-text search often provides search results of insufficient accuracy. In this paper, we will present an approach likely to improve search quality, through the use of Semantic Web, Text Mining, and Case Based Reasoning (CBR) technologies. Search results are more precise and complete because, in contrast to full-text search, the proposed knowledge-based search operates on the semantic layer

Context-Aware Information Retrieval for Enhanced Situation Awareness

In the coalition forces, users are increasingly challenged with the issues of information overload and correlation of information from heterogeneous sources. Users might need different pieces of information, ranging from information about a single building, to the resolution strategy of a global conflict. Sometimes, the time, location and past history of information access can also shape the information needs of users. Information systems need to help users pull together data from disparate sources according to their expressed needs (as represented by system queries), as well as less specific criteria. Information consumers have varying roles, tasks/missions, goals and agendas, knowledge and background, and personal preferences. These factors can be used to shape both the execution of user queries and the form in which retrieved information is packaged. However, full automation of this daunting information aggregation and customization task is not possible with existing approaches. In this paper we present an infrastructure for context-aware information retrieval to enhance situation awareness. The infrastructure provides each user with a customized, mission-oriented system that gives access to the right information from heterogeneous sources in the context of a particular task, plan and/or mission. The approach lays on five intertwined fundamental concepts, namely Workflow, Context, Ontology, Profile and Information Aggregation. The exploitation of this knowledge, using appropriate domain ontologies, will make it feasible to provide contextual assistance in various ways to the work performed according to a user’s taskrelevant information requirements. This paper formalizes these concepts and their interrelationships